{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7852cbd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        番泻苷A\n",
      "0  24.933937\n",
      "1  25.359890\n",
      "2  27.850932\n",
      "3  32.193367\n",
      "4  33.313904\n",
      "特征集样本数量： 403\n",
      "目标变量集样本数量： 403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30382\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n",
      "C:\\Users\\30382\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\activations\\leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 146ms/step\n",
      "ACLSTM MSE: 975.381517502395\n",
      "ACLSTM R2: 0.648872618250735\n",
      "ACLSTM MAE: 18.994929432625074\n",
      "ACLSTM RMSE: 31.23109856380968\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import pywt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D, LSTM, BatchNormalization, LeakyReLU, AveragePooling1D, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# 读取红外谱图数据\n",
    "file_path = 'F:\\\\研究\\\\番泻苷在线提取数据.xlsx'\n",
    "xls = pd.ExcelFile(file_path)\n",
    "ir_data = pd.read_excel(xls, '红外谱图', index_col='编号\\波数')\n",
    "\n",
    "# 使用PCA提取特征\n",
    "# 假设我们需要提取前10个主成分\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(ir_data)\n",
    "pca_features = pca.transform(ir_data)\n",
    "\n",
    "# 使用小波变换提取特征\n",
    "# 假设使用Daubechies 4小波基函数，并提取前3个近似系数\n",
    "wavelet = pywt.Wavelet('db4')\n",
    "coeffs = pywt.wavedec(np.array(ir_data), wavelet, level=3)\n",
    "wt_features = coeffs[0]\n",
    "\n",
    "# 将特征转换成DataFrame格式\n",
    "pca_features_df = pd.DataFrame(pca_features, columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7', 'PC8', 'PC9', 'PC10'])\n",
    "\n",
    "# 小波变换特征提取\n",
    "# coeffs[0]是第一层的近似系数，其形状是(244, 200)\n",
    "# 所以需要调整DataFrame的列数\n",
    "wt_features_df = pd.DataFrame(coeffs[0], columns=['WT' + str(i) for i in range(200)])\n",
    "\n",
    "# 读取'番泻苷含量'工作表\n",
    "targets = pd.read_excel(xls, '番泻苷含量')\n",
    "# 选取番泻苷A作为目标变量\n",
    "targets = targets[['番泻苷A']]\n",
    "# 查看目标变量的前几行\n",
    "print(targets.head())\n",
    "\n",
    "# 对特征集和目标变量集进行索引对齐\n",
    "pca_features_df, targets = pca_features_df.align(targets, join='inner', axis=0)\n",
    "\n",
    "# 再次检查对齐后的样本数量\n",
    "print(\"特征集样本数量：\", pca_features_df.shape[0])\n",
    "print(\"目标变量集样本数量：\", targets.shape[0])\n",
    "\n",
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(pca_features_df, targets, test_size=0.2, random_state=42)\n",
    "\n",
    "# 构建 ACLSTM 模型\n",
    "model_aclstm = Sequential()\n",
    "model_aclstm.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
    "model_aclstm.add(BatchNormalization())\n",
    "model_aclstm.add(LeakyReLU(alpha=0.01))\n",
    "model_aclstm.add(AveragePooling1D(pool_size=2))\n",
    "model_aclstm.add(Dropout(0.2))\n",
    "model_aclstm.add(Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "model_aclstm.add(BatchNormalization())\n",
    "model_aclstm.add(LeakyReLU(alpha=0.01))\n",
    "model_aclstm.add(AveragePooling1D(pool_size=2))\n",
    "model_aclstm.add(Dropout(0.2))\n",
    "model_aclstm.add(LSTM(128, return_sequences=True))\n",
    "model_aclstm.add(LSTM(128))\n",
    "model_aclstm.add(Dense(1))\n",
    "model_aclstm.compile(optimizer=Adam(learning_rate=1e-3), loss='mean_squared_error')\n",
    "\n",
    "import time\n",
    "\n",
    "# 开始训练前的时间\n",
    "start_train_time = time.time()\n",
    "\n",
    "# 训练模型\n",
    "history = model_aclstm.fit(X_train, y_train, epochs=1000, batch_size=10, validation_split=0.2, verbose=0)\n",
    "\n",
    "# 结束训练后的时间\n",
    "end_train_time = time.time()\n",
    "\n",
    "# 训练所需时间\n",
    "train_time = end_train_time - start_train_time\n",
    "\n",
    "# 开始测试前的时间\n",
    "start_test_time = time.time()\n",
    "\n",
    "# 测试模型\n",
    "y_pred_aclstm = model_aclstm.predict(X_test)\n",
    "\n",
    "# 结束测试后的时间\n",
    "end_test_time = time.time()\n",
    "\n",
    "# 测试所需时间\n",
    "test_time = end_test_time - start_test_time\n",
    "\n",
    "# 测试 ACLSTM 模型\n",
    "mse_aclstm = mean_squared_error(y_test, y_pred_aclstm)\n",
    "r2_aclstm = r2_score(y_test, y_pred_aclstm)\n",
    "# 输出模型性能指标\n",
    "print(\"ACLSTM MSE:\", mse_aclstm)\n",
    "print(\"ACLSTM R2:\", r2_aclstm)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# 计算回归指标\n",
    "mae_aclstm = mean_absolute_error(y_test, y_pred_aclstm)\n",
    "rmse_aclstm = np.sqrt(mean_squared_error(y_test, y_pred_aclstm))                  \n",
    "\n",
    "\n",
    "# 输出指标\n",
    "print(\"ACLSTM MAE:\", mae_aclstm)\n",
    "print(\"ACLSTM RMSE:\", rmse_aclstm)\n",
    "test_time = end_test_time - start_test_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ab00595",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch_geometric.nn import GCNConv\n",
    "\n",
    "class GCNN_LSTM_AT(nn.Module):\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super(GCNN_LSTM_AT, self).__init__()\n",
    "        self.gcn1 = GCNConv(num_features, 128)\n",
    "        self.gcn2 = GCNConv(128, 64)\n",
    "        self.conv1 = nn.Conv2d(1, 64, kernel_size=(5, 26), stride=1)\n",
    "        self.conv2 = nn.Conv2d(64, 64, kernel_size=(3, 27), stride=1)\n",
    "        self.lstm1 = nn.LSTM(64, 128, bidirectional=True, batch_first=True)\n",
    "        self.lstm2 = nn.LSTM(256, 128, bidirectional=True, batch_first=True)\n",
    "        self.attention = nn.MultiheadAttention(128, num_heads=8, batch_first=True)\n",
    "        self.fc = nn.Linear(128, num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = torch.relu(self.gcn1(x, edge_index))\n",
    "        x = torch.relu(self.gcn2(x, edge_index))\n",
    "        x = x.unsqueeze(1)\n",
    "        x = torch.relu(self.conv1(x))\n",
    "        x = torch.relu(self.conv2(x))\n",
    "        x = x.squeeze(1)\n",
    "        x = torch.relu(self.lstm1(x)[0])\n",
    "        x = torch.relu(self.lstm2(x)[0])\n",
    "        x, _ = self.attention(x, x, x)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "model = GCNN_LSTM_AT(num_features=701, num_classes=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a98f85a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "使用EarlyStopping回调函数，当验证集损失在连续几个epoch没有改善时，训练会提前终止。 这可以有效防止过拟合，并避免不必要的训练时间。\n",
    "设置patience参数，表示验证集损失连续多少个epoch没有改善时停止训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dcaf9b18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳epochs轮数: 134\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "ACLSTM MSE: 275.48884583323996\n",
      "ACLSTM R2: 0.9008268299093386\n",
      "ACLSTM MAE: 9.901417178930972\n",
      "ACLSTM RMSE: 16.597856663835845\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# 开始训练前的时间\n",
    "start_train_time = time.time()\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# 设置早停机制\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100)\n",
    "\n",
    "# 训练模型\n",
    "history = model_aclstm.fit(X_train, y_train, epochs=1000, batch_size=10, validation_split=0.2, verbose=0, callbacks=[early_stopping])\n",
    "\n",
    "# 获取最佳epochs轮数\n",
    "best_epoch = early_stopping.stopped_epoch + 1  # +1 因为stopped_epoch是从0开始的\n",
    "print(f\"最佳epochs轮数: {best_epoch}\")\n",
    "\n",
    "# 结束训练后的时间\n",
    "end_train_time = time.time()\n",
    "\n",
    "# 训练所需时间\n",
    "train_time = end_train_time - start_train_time\n",
    "\n",
    "# 开始测试前的时间\n",
    "start_test_time = time.time()\n",
    "\n",
    "# 测试模型\n",
    "y_pred_aclstm = model_aclstm.predict(X_test)\n",
    "\n",
    "# 结束测试后的时间\n",
    "end_test_time = time.time()\n",
    "\n",
    "# 测试所需时间\n",
    "test_time = end_test_time - start_test_time\n",
    "\n",
    "# 测试 ACLSTM 模型\n",
    "mse_aclstm = mean_squared_error(y_test, y_pred_aclstm)\n",
    "r2_aclstm = r2_score(y_test, y_pred_aclstm)\n",
    "# 输出模型性能指标\n",
    "print(\"ACLSTM MSE:\", mse_aclstm)\n",
    "print(\"ACLSTM R2:\", r2_aclstm)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# 计算回归指标\n",
    "mae_aclstm = mean_absolute_error(y_test, y_pred_aclstm)\n",
    "rmse_aclstm = np.sqrt(mean_squared_error(y_test, y_pred_aclstm))                  \n",
    "\n",
    "\n",
    "# 输出指标\n",
    "print(\"ACLSTM MAE:\", mae_aclstm)\n",
    "print(\"ACLSTM RMSE:\", rmse_aclstm)\n",
    "test_time = end_test_time - start_test_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ae60827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳epochs轮数: 134\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "ACLSTM MSE: 275.48884583323996\n",
      "ACLSTM R2: 0.9008268299093386\n",
      "ACLSTM MAE: 9.901417178930972\n",
      "ACLSTM RMSE: 16.597856663835845\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# 开始训练前的时间\n",
    "start_train_time = time.time()\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# 设置早停机制\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100)\n",
    "\n",
    "# 训练模型\n",
    "history = model_aclstm.fit(X_train, y_train, epochs=1000, batch_size=10, validation_split=0.2, verbose=0, callbacks=[early_stopping])\n",
    "\n",
    "# 获取最佳epochs轮数\n",
    "best_epoch = early_stopping.stopped_epoch + 1  # +1 因为stopped_epoch是从0开始的\n",
    "print(f\"最佳epochs轮数: {best_epoch}\")\n",
    "\n",
    "# 结束训练后的时间\n",
    "end_train_time = time.time()\n",
    "\n",
    "# 训练所需时间\n",
    "train_time = end_train_time - start_train_time\n",
    "\n",
    "# 开始测试前的时间\n",
    "start_test_time = time.time()\n",
    "\n",
    "# 测试模型\n",
    "y_pred_aclstm = model_aclstm.predict(X_test)\n",
    "\n",
    "# 结束测试后的时间\n",
    "end_test_time = time.time()\n",
    "\n",
    "# 测试所需时间\n",
    "test_time = end_test_time - start_test_time\n",
    "\n",
    "# 测试 ACLSTM 模型\n",
    "mse_aclstm = mean_squared_error(y_test, y_pred_aclstm)\n",
    "r2_aclstm = r2_score(y_test, y_pred_aclstm)\n",
    "# 输出模型性能指标\n",
    "print(\"ACLSTM MSE:\", mse_aclstm)\n",
    "print(\"ACLSTM R2:\", r2_aclstm)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# 计算回归指标\n",
    "mae_aclstm = mean_absolute_error(y_test, y_pred_aclstm)\n",
    "rmse_aclstm = np.sqrt(mean_squared_error(y_test, y_pred_aclstm))                  \n",
    "\n",
    "\n",
    "# 输出指标\n",
    "print(\"ACLSTM MAE:\", mae_aclstm)\n",
    "print(\"ACLSTM RMSE:\", rmse_aclstm)\n",
    "test_time = end_test_time - start_test_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c010c44c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 24.525914907455444 seconds\n",
      "Testing time: 0.06414103507995605 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印时间\n",
    "print(f\"Training time: {train_time} seconds\")\n",
    "print(f\"Testing time: {test_time} seconds\")\n",
    "\n",
    "# 绘制训练和验证损失\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Train loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.title('Training and Validation Loss Over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d052a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        番泻苷B\n",
      "0  65.048774\n",
      "1  62.287028\n",
      "2  65.272927\n",
      "3  75.099722\n",
      "4  78.549335\n",
      "特征集样本数量： 403\n",
      "目标变量集样本数量： 403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30382\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n",
      "C:\\Users\\30382\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\activations\\leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 145ms/step\n",
      "ACLSTM MSE: 360.7000722836827\n",
      "ACLSTM R2: 0.9327478147284564\n",
      "ACLSTM MAE: 10.644359799273142\n",
      "ACLSTM RMSE: 18.992105525288203\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import pywt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D, LSTM, BatchNormalization, LeakyReLU, AveragePooling1D, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# 读取红外谱图数据\n",
    "file_path = 'F:\\\\研究\\\\番泻苷在线提取数据.xlsx'\n",
    "xls = pd.ExcelFile(file_path)\n",
    "ir_data = pd.read_excel(xls, '红外谱图', index_col='编号\\波数')\n",
    "\n",
    "# 使用PCA提取特征\n",
    "# 假设我们需要提取前10个主成分\n",
    "pca = PCA(n_components=10)\n",
    "pca.fit(ir_data)\n",
    "pca_features = pca.transform(ir_data)\n",
    "\n",
    "# 使用小波变换提取特征\n",
    "# 假设使用Daubechies 4小波基函数，并提取前3个近似系数\n",
    "wavelet = pywt.Wavelet('db4')\n",
    "coeffs = pywt.wavedec(np.array(ir_data), wavelet, level=3)\n",
    "wt_features = coeffs[0]\n",
    "\n",
    "# 将特征转换成DataFrame格式\n",
    "pca_features_df = pd.DataFrame(pca_features, columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7', 'PC8', 'PC9', 'PC10'])\n",
    "\n",
    "# 小波变换特征提取\n",
    "# coeffs[0]是第一层的近似系数，其形状是(244, 200)\n",
    "# 所以需要调整DataFrame的列数\n",
    "wt_features_df = pd.DataFrame(coeffs[0], columns=['WT' + str(i) for i in range(200)])\n",
    "\n",
    "# 读取'番泻苷含量'工作表\n",
    "targets = pd.read_excel(xls, '番泻苷含量')\n",
    "# 选取番泻苷A作为目标变量\n",
    "targets = targets[['番泻苷B']]\n",
    "# 查看目标变量的前几行\n",
    "print(targets.head())\n",
    "\n",
    "# 对特征集和目标变量集进行索引对齐\n",
    "pca_features_df, targets = pca_features_df.align(targets, join='inner', axis=0)\n",
    "\n",
    "# 再次检查对齐后的样本数量\n",
    "print(\"特征集样本数量：\", pca_features_df.shape[0])\n",
    "print(\"目标变量集样本数量：\", targets.shape[0])\n",
    "\n",
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(pca_features_df, targets, test_size=0.2, random_state=42)\n",
    "\n",
    "# 构建 ACLSTM 模型\n",
    "model_aclstm = Sequential()\n",
    "model_aclstm.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
    "model_aclstm.add(BatchNormalization())\n",
    "model_aclstm.add(LeakyReLU(alpha=0.01))\n",
    "model_aclstm.add(AveragePooling1D(pool_size=2))\n",
    "model_aclstm.add(Dropout(0.2))\n",
    "model_aclstm.add(Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "model_aclstm.add(BatchNormalization())\n",
    "model_aclstm.add(LeakyReLU(alpha=0.01))\n",
    "model_aclstm.add(AveragePooling1D(pool_size=2))\n",
    "model_aclstm.add(Dropout(0.2))\n",
    "model_aclstm.add(LSTM(128, return_sequences=True))\n",
    "model_aclstm.add(LSTM(128))\n",
    "model_aclstm.add(Dense(1))\n",
    "model_aclstm.compile(optimizer=Adam(learning_rate=1e-3), loss='mean_squared_error')\n",
    "\n",
    "import time\n",
    "\n",
    "# 开始训练前的时间\n",
    "start_train_time = time.time()\n",
    "\n",
    "# 训练模型\n",
    "history = model_aclstm.fit(X_train, y_train, epochs=1000, batch_size=10, validation_split=0.2, verbose=0)\n",
    "\n",
    "# 结束训练后的时间\n",
    "end_train_time = time.time()\n",
    "\n",
    "# 训练所需时间\n",
    "train_time = end_train_time - start_train_time\n",
    "\n",
    "# 开始测试前的时间\n",
    "start_test_time = time.time()\n",
    "\n",
    "# 测试模型\n",
    "y_pred_aclstm = model_aclstm.predict(X_test)\n",
    "\n",
    "# 结束测试后的时间\n",
    "end_test_time = time.time()\n",
    "\n",
    "# 测试所需时间\n",
    "test_time = end_test_time - start_test_time\n",
    "\n",
    "# 测试 ACLSTM 模型\n",
    "mse_aclstm = mean_squared_error(y_test, y_pred_aclstm)\n",
    "r2_aclstm = r2_score(y_test, y_pred_aclstm)\n",
    "mae_aclstm = mean_absolute_error(y_test, y_pred_aclstm)\n",
    "rmse_aclstm = np.sqrt(mean_squared_error(y_test, y_pred_aclstm))        \n",
    "\n",
    "# 输出模型性能指标\n",
    "print(\"ACLSTM MSE:\", mse_aclstm)\n",
    "print(\"ACLSTM R2:\", r2_aclstm)\n",
    "print(\"ACLSTM MAE:\", mae_aclstm)\n",
    "print(\"ACLSTM RMSE:\", rmse_aclstm)\n",
    "\n",
    "# 打印时间\n",
    "print(f\"Training time: {train_time} seconds\")\n",
    "print(f\"Testing time: {test_time} seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48ee8274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳epochs轮数: 147\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "ACLSTM MSE: 308.9037144793692\n",
      "ACLSTM R2: 0.9424051963568894\n",
      "ACLSTM MAE: 10.712296066089728\n",
      "ACLSTM RMSE: 17.575656871917168\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# 开始训练前的时间\n",
    "start_train_time = time.time()\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# 设置早停机制\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100)\n",
    "\n",
    "# 训练模型\n",
    "history = model_aclstm.fit(X_train, y_train, epochs=1000, batch_size=10, validation_split=0.2, verbose=0, callbacks=[early_stopping])\n",
    "\n",
    "# 获取最佳epochs轮数\n",
    "best_epoch = early_stopping.stopped_epoch + 1  # +1 因为stopped_epoch是从0开始的\n",
    "print(f\"最佳epochs轮数: {best_epoch}\")\n",
    "\n",
    "# 结束训练后的时间\n",
    "end_train_time = time.time()\n",
    "\n",
    "# 训练所需时间\n",
    "train_time = end_train_time - start_train_time\n",
    "\n",
    "# 开始测试前的时间\n",
    "start_test_time = time.time()\n",
    "\n",
    "# 测试模型\n",
    "y_pred_aclstm = model_aclstm.predict(X_test)\n",
    "\n",
    "# 结束测试后的时间\n",
    "end_test_time = time.time()\n",
    "\n",
    "test_time = end_test_time - start_test_time\n",
    "\n",
    "# 测试 ACLSTM 模型\n",
    "mse_aclstm = mean_squared_error(y_test, y_pred_aclstm)\n",
    "r2_aclstm = r2_score(y_test, y_pred_aclstm)\n",
    "mae_aclstm = mean_absolute_error(y_test, y_pred_aclstm)\n",
    "rmse_aclstm = np.sqrt(mean_squared_error(y_test, y_pred_aclstm))        \n",
    "\n",
    "# 输出模型性能指标\n",
    "print(\"ACLSTM MSE:\", mse_aclstm)\n",
    "print(\"ACLSTM R2:\", r2_aclstm)\n",
    "print(\"ACLSTM MAE:\", mae_aclstm)\n",
    "print(\"ACLSTM RMSE:\", rmse_aclstm)\n",
    "\n",
    "# 打印时间\n",
    "print(f\"Training time: {train_time} seconds\")\n",
    "print(f\"Testing time: {test_time} seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f6ac1a00",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 26.527350425720215 seconds\n",
      "Testing time: 0.06242942810058594 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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L/HVflxo0RdP6vmo4HA6MGDECX3/9Ne6++2589tlnKC4uVpq8Nfz5otVJXjTr+vjjj2Gz2fDll1+iqqoKl156qbLNu+++i0mTJqFz5854/fXXMX/+fBQXF+PMM8/UdZTcgw8+iNtuuw1DhgzBu+++iwULFqC4uBi9e/eO2gg7vf8u1GjVqhXWrFmDL774QukPMGbMGK817kOGDMHWrVvxxhtv4Pjjj8drr72GgQMH4rXXXgv42D179sSmTZtQV1fnd5vff/8dVqvV62TMRRddhMWLF+PQoUOoq6vDF198gYkTJyoTJ8Txueyyy3xm1YuLi3Hqqad6PU8o2fp4oOZv58ILL8S2bdvw7LPPorCwEI899hh69+7dqHKEiCjesHkeEVETtWTJEhw+fBiffPIJhgwZoty+fft2A/fKrVWrVkhNTcWWLVsafc/XbQ2tW7cOf/31F9566y2v2dvBupYH0r59e3z77beorq72ytpv2rQppMe59NJLMX/+fHzzzTeYM2cOsrOzMX78eOX7H330ETp16oRPPvnEq9zYVzmymn0GgM2bN6NTp07K7QcPHmyUBf/oo48wbNgwvP766163l5eXo2XLlsrXaiYSeD7/okWLGpWgi6UeYv+ioX379vj999/hdDq9MsW+9iU5ORnjx4/H+PHj4XQ6ceONN+Lll1/Gv//9b6VipHnz5rjqqqtw1VVXobq6GkOGDMGMGTMwZcoUv/tw9tlnY/ny5Zg7d67P0XE7duzA0qVLMXz4cK/A+6KLLsLMmTPx8ccfIz8/H5WVlbj44ouV7+fl5SErKwsOhwPDhw8P/5ekgX379uHo0aNeWfu//voLAJSJCWqPRefOnbFgwQKUlZWpytqr0bp1a9x444248cYbceDAAQwcOBAPPPCA6iU+RESxiBl7IqImSmS3PLNZ9fX1eOGFF4zaJS8WiwXDhw/HZ5995rUedsuWLaqya75+PkmSvEaWhWrs2LGw2+148cUXldscDgeeffbZkB5nwoQJSE9PxwsvvIBvvvkG5513HlJTUwPu+y+//ILly5eHvM/Dhw+H1WrFs88+6/V4Tz31VKNtLRZLo8z43LlzsXfvXq/bRMCmZszf2LFj4XA48Nxzz3nd/uSTT8JkMkU1mBo7dixKSkq8StrtdjueffZZZGZmKss0Dh8+7HU/s9mMvn37AoCSaW+4TWZmJrp06RIwEw8A1113HVq1aoU777yzUQl4bW0trrrqKkiShOnTp3t9r2fPnujTpw8++OADfPDBB2jdurXXCTmLxYKJEyfi448/xvr16xs978GDBwPul5bsdjtefvll5ev6+nq8/PLLyMvLw6BBgwCoPxYTJ06EJEmYOXNmo+cJtYrD4XA0WlLSqlUrFBYWBj1uRESxjhl7IqIm6pRTTkGzZs1w5ZVX4pZbboHJZMI777wT1ZLnYGbMmIGFCxfi1FNPxQ033KAEiMcffzzWrFkT8L49evRA586dcccdd2Dv3r3Izs7Gxx9/HNFa7fHjx+PUU0/FP//5T+zYsQO9evXCJ598EvL688zMTEyYMEFZZ+9Zhg/IWd1PPvkE5557LsaNG4ft27fjpZdeQq9evVBdXR3Sc+Xl5eGOO+7AQw89hLPPPhtjx47F6tWr8c0333hl4cXzzpo1C1dddRVOOeUUrFu3Dv/73/+8Mv2AnEXNzc3FSy+9hKysLGRkZOCkk07yuWZ7/PjxGDZsGO655x7s2LED/fr1w8KFC/H5559j2rRpjWa5R+rbb79FbW1to9snTJiAa6+9Fi+//DImTZqEVatWoUOHDvjoo4+wbNkyPPXUU0pFwZQpU1BWVoYzzzwTbdq0wc6dO/Hss8+if//+yhrwXr16YejQoRg0aBCaN2+OlStXKmPUAmnRogU++ugjjBs3DgMHDsSUKVPQq1cvlJSUYPbs2diyZQuefvppn+MDL7roIkyfPh2pqamYPHlyo/XpDz/8MBYvXoyTTjoJ11xzDXr16oWysjL89ttvWLRoEcrKysL9tQKQs+7vvvtuo9vz8/O9RvwVFhbikUcewY4dO9CtWzd88MEHWLNmDV555RVlDKbaYzFs2DBcfvnleOaZZ7B582aMHj0aTqcTS5cuxbBhw4L+vj1VVVWhTZs2OP/889GvXz9kZmZi0aJFWLFiBZ544omIfjdERIaLdht+IiLSj79xd7179/a5/bJly6STTz5ZSktLkwoLC6W77rpLWrBggQRAWrx4sbKdv3F3vkaLocH4NX/j7nyNnGrfvr3X+DVJkqRvv/1WGjBggJScnCx17txZeu2116Tbb79dSk1N9fNbcPvjjz+k4cOHS5mZmVLLli2la665RhmB5Tmq7corr5QyMjIa3d/Xvh8+fFi6/PLLpezsbCknJ0e6/PLLpdWrV6sedyd8/fXXEgCpdevWjUbMOZ1O6cEHH5Tat28vpaSkSAMGDJC++uqrRsdBkoKPu5MkSXI4HNLMmTOl1q1bS2lpadLQoUOl9evXN/p919bWSrfffruy3amnniotX75cOuOMM6QzzjjD63k///xzqVevXsroQfGz+9rHqqoq6dZbb5UKCwslq9Uqde3aVXrssce8xpWJn0Xt30VD4m/S37933nlHkiRJKi0tla666iqpZcuWUnJystSnT59Gx+2jjz6SRo4cKbVq1UpKTk6W2rVrJ1133XXS/v37lW3uv/9+6cQTT5Ryc3OltLQ0qUePHtIDDzygjHMLZvv27dI111wjtWvXTrJarVLLli2lv/3tb9LSpUv93mfz5s3Kz/Pjjz/63Ka0tFSaOnWq1LZtW8lqtUoFBQXSWWedJb3yyivKNmLc3dy5c1XtqyQFHnfn+bchXm9WrlwpFRUVSampqVL79u2l5557zue+BjsWkiSPf3zsscekHj16SMnJyVJeXp40ZswYadWqVV77F+xvp66uTrrzzjulfv36SVlZWVJGRobUr18/6YUXXlD9eyAiilUmSYqh1AwREZEKEyZMCGvUGBHpa+jQoTh06JDP5QBERKQfrrEnIqKYVlNT4/X15s2bMW/ePAwdOtSYHSIiIiKKMVxjT0REMa1Tp06YNGkSOnXqhJ07d+LFF19EcnIy7rrrLqN3jYiIiCgmMLAnIqKYNnr0aLz33nsoKSlBSkoKioqK8OCDD3rN+CYiIiJqyrjGnoiIiIiIiCiOcY09ERERERERURxjYE9EREREREQUx7jGXgWn04l9+/YhKysLJpPJ6N0hIiIiIiKiBCdJEqqqqlBYWAizOXBOnoG9Cvv27UPbtm2N3g0iIiIiIiJqYnbv3o02bdoE3IaBvQpZWVkA5F9odna2wXsTmM1mw8KFCzFy5EhYrVajd4ciwGOZWHg8EwuPZ2Lh8UwsPJ6JhcczcfBYhq6yshJt27ZV4tFAGNirIMrvs7Oz4yKwT09PR3Z2Nv/DxDkey8TC45lYeDwTC49nYuHxTCw8nomDxzJ8apaDs3keERERERERURxjYE9EREREREQUxxjYExEREREREcUxrrEnIiIiIiIKgcPhgM1mM3o34orNZkNSUhJqa2vhcDiM3p2YYbVaYbFYIn4cBvZEREREREQqVVdXY8+ePZAkyehdiSuSJKGgoAC7d+9W1QyuqTCZTGjTpg0yMzMjehwG9kRERERERCo4HA7s2bMH6enpyMvLY4AaAqfTierqamRmZsJs5opwQD7ZcfDgQezZswddu3aNKHPPwJ6IiIiIiEgFm80GSZKQl5eHtLQ0o3cnrjidTtTX1yM1NZWBvYe8vDzs2LEDNpstosCev1EiIiIiIqIQMFNPWtHqb4mBPREREREREVEcY2BPREREREREFMcY2BMREREREVFIOnTogKeeesrwxyAZA3siIiIiIqIEZTKZAv6bMWNGWI+7YsUKXHvttdruLIWNXfGJiIiIiIgS1P79+5XrH3zwAaZPn45NmzYpt3nOT5ckCQ6HA0lJwcPEvLw8bXeUIsKMPRERERERURgkScKxersh/yRJUrWPBQUFyr+cnByYTCbl6z///BNZWVn45ptvMGjQIKSkpODHH3/E1q1bcc455yA/Px+ZmZk44YQTsGjRIq/HbVhGbzKZ8Nprr+Hcc89Feno6unbtii+++CKk3+euXbtwzjnnIDMzE9nZ2bjwwgtRWlqqfH/t2rUYNmwYsrKykJ2djUGDBmHlypUAgJ07d2L8+PFo1qwZMjIy0Lt3b8ybNy+k549nzNgTERERERGFocbmQK/pCwx57j9mjUJ6sjbh3D//+U88/vjj6NSpE5o1a4bdu3dj7NixeOCBB5CSkoK3334b48ePx6ZNm9CuXTu/jzNz5kw8+uijeOyxx/Dss8/i0ksvxc6dO9G8efOg++B0OpWg/vvvv4fdbsfUqVNx0UUXYcmSJQCASy+9FAMGDMCLL74Ii8WCNWvWwGq1AgCmTp2K+vp6/PDDD8jIyMAff/zhVY2Q6BjYExERERERNWGzZs3CiBEjlK+bN2+Ofv36KV/fd999+PTTT/HFF1/gpptu8vs4kyZNwiWXXAIAePDBB/HMM8/g119/xejRo4Puw7fffot169Zh+/btaNu2LQDg7bffRu/evbFixQqccMIJ2LVrF+6880706NEDANC1a1fl/rt27cLEiRPRp08fAECnTp1C+A3EPwb2lPiOHgaOHQbyuhm9J0RERESUQNKsFvwxa5Rhz62VwYMHe31dXV2NGTNm4Ouvv8b+/ftht9tRU1ODXbt2BXycvn37KtczMjKQnZ2NAwcOqNqHjRs3om3btkpQDwC9evVCbm4uNm7ciBNOOAG33XYbpkyZgnfeeQfDhw/HBRdcgM6dOwMAbrnlFtxwww1YuHAhhg8fjokTJ3rtT6LjGntKbA478OYY4MUioGKv0XtDRERERAnEZDIhPTnJkH8mk0mznyMjI8Pr6zvuuAOffvopHnzwQSxduhRr1qxBnz59UF9fH/BxRFm85+/H6XRqtp8zZszAhg0bMG7cOHz33Xfo1asXPv30UwDAlClTsG3bNlx++eVYt24dBg8ejGeffVaz5451DOwpsf35JXBoE+C0A+WBzzASERERERGwbNkyTJo0Ceeeey769OmDgoIC7NixQ9fn7NmzJ3bv3o3du3crt/3xxx8oLy9Hr169lNu6deuGW2+9FQsXLsR5552HN998U/le27Ztcf311+OTTz7B7bffjldffVXXfY4lDOwpcUkSsOwZ99f2GuP2hYiIiIgoTnTt2hWffPIJ1qxZg7Vr1+Lvf/+7ppl3X4YPH44+ffrg0ksvxW+//YZff/0VV1xxBc444wwMHjwYNTU1uOmmm7BkyRLs3LkTy5Ytw4oVK9CzZ08AwLRp07BgwQJs374dv/32GxYvXqx8rylgYE+Ja9dyYN9v7q9ttcbtCxERERFRnPjvf/+LZs2a4ZRTTsH48eMxatQoDBw4UNfnNJlM+Pzzz9GsWTMMGTIEw4cPR6dOnfDBBx8AACwWCw4fPowrrrgC3bp1w4UXXogxY8Zg5syZAACHw4GpU6eiZ8+eGD16NLp164YXXnhB132OJWyeR4nrp+e8v2bGnoiIiIiasEmTJmHSpEnK10OHDoUkSY2269ChA7777juv26ZOner1dcPSfF+PU15eHnB/Gj5Gu3bt8Pnnn/vcNjk5Ge+9957fx2pK6+l9YcaeEtOhzcCmefL15q5RF8zYExERERFRAmLGnhLT8ucBSEC3MYDZApRtY8aeiIiIiIgSEjP2lHiOHgLWusp0TrkZSEqVr9vrjNsnIiIiIiIinTCwp8Sz4jXAXgsUDgTanwJYXYG9jRl7IiIiIiJKPAzsKbHYaoBfX5Gvn3ITYDIBSWny13ausSciIiIiosTDwJ4Sy9r3gWOHgZx2QM9z5NuYsSciIiIiogTGwJ4Sh9MJLHeNuDv5BsDi6g3JjD0RERERESUwBvaUOP6aDxzeAqTkAAMvd9+uZOwZ2BMRERERUeJhYE+JY8Mn8uXAy4GULPftSsaepfhERERERJR4GNhT4qirki/zunvfzow9EREREVFEhg4dimnTpilfd+jQAU899VTA+5hMJnz22WcRP7dWjxPIjBkz0L9/f12fQ08M7ClxiDX0Ym69oMyxZ8aeiIiIiJqW8ePHY/To0T6/t3TpUphMJvz+++8hP+6KFStw7bXXRrp7XvwF1/v378eYMWM0fa5Ew8CeEoe9Tr60JHvfrgT2ddHdHyIiIiIig02ePBnFxcXYs2dPo++9+eabGDx4MPr27Rvy4+bl5SE9PV2LXQyqoKAAKSkpUXmueMXAnhKHv4y91bXGnuPuiIiIiEhLkgTUHzXmnySp2sWzzz4beXl5mD17ttft1dXVmDt3LiZPnozDhw/jkksuwXHHHYf09HT06dMH7733XsDHbViKv3nzZgwZMgSpqano1asXiouLG93nP//5D3r06IH09HR06tQJ//73v2Gz2QAAs2fPxsyZM7F27VqYTCaYTCZlnxuW4q9btw5nnnkm0tLS0KJFC1x77bWorq5Wvj9p0iRMmDABjz/+OFq3bo0WLVpg6tSpynOp4XQ6MWvWLLRp0wYpKSno378/5s+fr3y/vr4eN910E1q3bo3U1FS0b98eDz30EABAkiTMmDED7dq1Q0pKCgoLC3HLLbeofu5wJOn66ETRZK+XL5ManM1TMvZcY09EREREGrIdAx4sNOa5/28fkJwRdLOkpCRcccUVmD17Nu655x6YTCYAwNy5c+FwOHDJJZeguroagwYNwt13343s7Gx8/fXXuPzyy9G5c2eceOKJQZ/D6XTivPPOQ35+Pn755RdUVFR4rccXsrKy8MYbb6BNmzZYt24drrnmGmRlZeGuu+7CRRddhPXr12P+/PlYtGgRACAnJ6fRYxw9ehSjRo1CUVERVqxYgQMHDmDKlCm46aabvE5eLF68GK1bt8bixYuxZcsWXHTRRejfvz+uueaaoD8PADz99NN44okn8PLLL2PAgAF444038Le//Q0bNmxA165d8cwzz+CLL77Ahx9+iHbt2mH37t3YvXs3AODjjz/Gk08+iffffx+9e/dGSUkJ1q5dq+p5w8XAnhIHM/ZERERERI1cffXVeOyxx/D9999j6NChAOQy/IkTJyInJwc5OTm44447lO1vvvlmLFiwAB9++KGqwH7RokX4888/sWDBAhQWyic6HnzwwUbr4u+44w5kZ2fDbDajQ4cOuOOOO/D+++/jrrvuQlpaGjIzM5GUlISCggK/zzVnzhzU1tbi7bffRkaGfGLjueeew/jx4/HII48gPz8fANCsWTM899xzsFgs6NGjB8aNG4dvv/1WdWD/+OOP4+6778bFF18MAHjkkUewePFiPPXUU3j++eexa9cudO3aFaeddhpMJhPat2+v3HfXrl0oKCjA8OHDYbVa0a5dO1W/x0gwsKfEIdbQJ/lbY8+MPRERERFpyJouZ86Nem6VevTogVNOOQVvvPEGhg4dii1btmDp0qWYNWsWAMDhcODBBx/Ehx9+iL1796K+vh51dXWq19Bv3LgRbdu2VYJ6ACgqKmq03SeffILXX38dW7duRXV1Nex2O7Kzs1X/HOK5+vXrpwT1AHDqqafC6XRi06ZNSmDfu3dvWCwWZZvWrVtj3bp1qp6jsrIS+/btw6mnnup1+6mnnqpk3idNmoQRI0age/fuGD16NM4++2yMHDkSAHDBBRfgqaeeQqdOnTB69GiMHTsW48ePR1KSfuE319hT4mDGnoiIiIiiyWSSy+GN+OcqqVdr8uTJ+Pjjj1FVVYU333wTnTt3xhlnnAEAeOyxx/D000/j7rvvxuLFi7FmzRqMGjUK9fX1mv2qli9fjmuvvRZjxozBV199hdWrV+Oee+7R9Dk8Wa1Wr69NJhOcTqdmjz9w4EBs374d9913H2pqanDhhRfi/PPPBwC0bdsWmzZtwgsvvIC0tDTceOONGDJkSEhr/EPFwJ4Sh0Ossfc37o4ZeyIiIiJqmi688EKYzWbMmTMHb7/9Nq6++mplvf2yZctwzjnn4LLLLkO/fv3QqVMn/PXXX6ofu2fPnti9ezf279+v3Pbzzz97bbN8+XK0bdsW//d//4fBgweja9eu2Llzp9c2ycnJcDgcQZ9r7dq1OHr0qHLbsmXLYDab0b17d9X7HEh2djYKCwuxbNkyr9uXLVuGXr16eW130UUX4dVXX8UHH3yAjz/+GGVlZQCAtLQ0jB8/Hs888wyWLFmC5cuXq64YCEfMBPYPP/wwTCaTV5OF2tpaTJ06FS1atEBmZiYmTpyI0tJSr/vt2rUL48aNQ3p6Olq1aoU777wTdrvda5slS5Zg4MCBSElJQZcuXRp1hKQEoWTsGzTPExl7e63q7qFERERERIkkMzMTF110Ef71r39h//79mDRpkvK9rl27ori4GD/99BM2btyI6667rlHcFcjw4cPRrVs3XHnllVi7di2WLl2Ke+65x2ubLl26YM+ePXj//fexdetWPPPMM/j000+9tunQoQO2b9+ONWvW4NChQ6irazyu+tJLL0VqaiquvPJKrF+/HosXL8bNN9+Myy+/XCnD18Kdd96JRx55BB988AE2bdqEf/7zn1izZg3+8Y9/AAD++9//4r333sOff/6Jv/76C3PnzkVBQQFyc3Mxe/ZsvP7661i/fj22bduGd999F2lpaV7r8LUWE4H9ihUr8PLLLzean3jrrbfiyy+/xNy5c/H9999j3759OO+885TvOxwOjBs3DvX19fjpp5/w1ltvYfbs2Zg+fbqyzfbt2zFu3DgMGzYMa9aswbRp0zBlyhQsWLAgaj8fRYHT6c7YWxp2xff4mll7IiIiImqiJk+ejCNHjmDUqFFe6+HvvfdeDBw4EKNGjcLQoUNRUFCACRMmqH5cs9mMTz/9FDU1NTjxxBMxZcoUPPDAA17b/O1vf8MNN9yAW265Bf3798dPP/2Ef//7317bTJw4EaNHj8awYcOQl5fnc+Reeno6FixYgLKyMpxwwgk4//zzcdZZZ+G5554L7ZcRxC233ILbbrsNt99+O/r06YP58+fjiy++QNeuXQHIHf4fffRRDB48GCeccAJ27NiBefPmwWw2Izc3F6+++ipOPfVU9O3bF4sWLcKXX36JFi1aaLqPnkySZGwKs7q6GgMHDsQLL7yA+++/H/3798dTTz2FiooK5OXlYc6cOcpahT///BM9e/bE8uXLcfLJJ+Obb77B2WefjX379ilnZ1566SXcfffdOHjwIJKTk3H33Xfj66+/xvr165XnvPjii1FeXu41hzCQyspK5OTkoKKiIuTmDtFms9kwb948jB07ttG6koRmqwUecJ2h++duINXjONnrgfvz5Ot37wDSmkV998LRZI9lguLxTCw8nomFxzOx8Hgmllg7nrW1tdi+fTs6duyI1NTU4HcghdPpRGVlpdIVn2SB/qZCiUMN74o/depUjBs3DsOHD8f999+v3L5q1SrYbDYMHz5cua1Hjx5o166dEtgvX74cffr08Sq5GDVqFG644QZs2LABAwYMwPLly70eQ2zja66iUFdX51X2UVlZCUB+YdGz4YEWxP7F+n5qrrYa4qXeBgvg+fNLQJLJDJPkhK2mCkjKNGQXQ9Vkj2WC4vFMLDyeiYXHM7HweCaWWDueNpsNkiTB6XRq2oitKRD5ZPH7I5nT6YQkSbDZbF5d/IHQ/u4NDezff/99/Pbbb1ixYkWj75WUlCA5ORm5ublet+fn56OkpETZpuE6CvF1sG0qKytRU1ODtLS0Rs/90EMPYebMmY1uX7hwoeqRD0YrLi42eheiKsVWjtEAJJgwb35xoy6h40xWJEl1WLJoPo6laLf2Jhqa2rFMdDyeiYXHM7HweCYWHs/EEivHU8xYr66u1q2be6KrqqoyehdiSn19PWpqavDDDz806hV37Ngx1Y9jWGC/e/du/OMf/0BxcXHMlbH861//wm233aZ8XVlZibZt22LkyJFxUYpfXFyMESNGxES5UtSU7wLWA0hKwdhx4xp927IpEzhWh6Gnngy06hn9/QtDkz2WCYrHM7HweCYWHs/EwuOZWGLteNbW1mL37t3IzMyMuRgm1kmShKqqKmRlZSnd+En+m0pLS8OQIUN8luKrZVhgv2rVKhw4cAADBw5UbnM4HPjhhx/w3HPPYcGCBaivr0d5eblX1r60tBQFBQUAgIKCAvz6669ejyu6N3pu07CjY2lpKbKzs31m6wEgJSUFKSkpjW63Wq0x8YKiRjztqyZMcjmPKSnF98+dJB9rK2xAnP1emtyxTHA8nomFxzOx8HgmFh7PxBIrx9PhcMBkMsFsNnOdeIhE+b34/ZHMbDbDZDL5/BsP5W/esN/oWWedhXXr1mHNmjXKv8GDB+PSSy9VrlutVnz77bfKfTZt2oRdu3ahqKgIAFBUVIR169bhwIEDyjbFxcXIzs5W5gsWFRV5PYbYRjwGJQhl1J2fM6dW1+02dsUnIiIiosgY3H+cEohWf0uGZeyzsrJw/PHHe92WkZGBFi1aKLdPnjwZt912G5o3b47s7GzcfPPNKCoqwsknnwwAGDlyJHr16oXLL78cjz76KEpKSnDvvfdi6tSpSsb9+uuvx3PPPYe77roLV199Nb777jt8+OGH+Prrr6P7A5O+7K5mhw1n2AtJYpZ9TXT2h4iIiIgSjmhuVl9f77f6lygUoldDw8Z5oTK8K34gTz75JMxmMyZOnIi6ujqMGjUKL7zwgvJ9i8WCr776CjfccAOKioqQkZGBK6+8ErNmzVK26dixI77++mvceuutePrpp9GmTRu89tprGDVqlBE/EulFZOwbzrAXmLEnIiIiogglJSUhPT0dBw8ehNVqZUl5CJxOJ+rr61FbW8vfm4vT6cTBgweRnp6OpKTIQvOYCuyXLFni9XVqaiqef/55PP/8837v0759e8ybNy/g4w4dOhSrV6/WYhcpVikZez+l+OJ2OwN7IiIiIgqPyWRC69atsX37duzcudPo3YkrkiQpU8nYPM/NbDajXbt2Ef9OYiqwJwpb0FJ8BvZEREREFLnk5GR07dqV4+5CZLPZ8MMPP2DIkCEx0QgxViQnJ2tSwcDAnhKD6uZ5XGNPRERERJExm80cdxcii8UCu92O1NRUBvY64OIGSgxKxj7Z9/eV5nnM2BMRERERUWJhYE/R5bADNeU6PG6QNfZsnkdERERERAmKgT1F14eXA493A8p3afu4HHdHRERERERNFAN7ip6jh4BN38jZ9cNbtH1s1WvsmbEnIiIiIqLEwsCeouevBQAk+bpd4y6iImNvCbbGnhl7IiIiIiJKLAzsKXr++sZ9XayJ10rQOfauEn1m7ImIiIiIKMEwsKfosNUCW75zf615xl6U4vtZY29lV3wiIiIiIkpMDOwpOnYsBWxH3V9HPWPvup2BPRERERERJRgG9hQdm+Z5f+3QK2PvZ429yNjbuMaeiIiIiIgSCwN70p8kyd3wASCtmXypdSm+OFHAjD0RERERETUxDOxJf/vXAFX7AWsG0GmofJvmpfjBxt0xY09ERERERImJgT3pT2Tru5wJpGTJ16M+7o4ZeyIiIiIiSkwM7El/Yn1997GAxdW1PtrN85ixJyIiIiKiBMXAnvRVvhsoWQeYzEDXke5xdJo3zxOBvZ9xd8zYExERERFRgmJgT/r6a7582fYkIKMlYLHKX+s2x57N84iIiIiIqGlhYE/6EmX43UbLl7qX4vsbd+cK7G0M7ImIiIiIKLEwsCf91FYC25fK17uPlS9F4K35uLsga+yT0tzbOZ3aPjcREREREZGBGNiTfrZ+BzhtQPPOQMuu8m26Z+z9rLG3egT8LMcnIiIiIqIEwsCe9CPG3HUfA5hM8nXdmucFW2Of1nhbIiIiIiKiBMDAnvThsAObF8jXRRk+oGPzPDHH3k/G3pIEmJPk6xx5R0RERERECYSBPelj9y9AzREgrZncEV8wqhQfcGftmbEnIiIiIqIEwsCe9CGy9V1HytlyQY/meZIUvHke4NEZnxl7IiIiIiJKHAzsSR9VJfJl/vHet+uRsbd7PBYz9kRERERE1MQwsCd9OGzypVhTL+jRPM8zUA8Y2Kc03p6IiIiIiCjOMbAnfTjt8qU5yft2PZrneZ4ksCT7304pxWdgT0REREREiYOBPenDb2CvRym+x6g7MVbPF6UUn2vsiYiIiIgocTCwJ334LcXXoXmemo74ADP2RERERESUkBjYkz6crsDe3CCw1zNj72+GvcCMPRERERERJSAG9qQPp0O+tDQoxdeleZ7rsQKNugOYsSciIiIiooTEwJ70IUrxo9E8T1ljz4w9ERERERE1PQzsSR9GlOIzY09ERERERE0QA3vSR7A59pITcNi1eS6leV6AUXcAM/ZERERERJSQGNiTPsQa+0al+B7Bt1ZZe/E4wTL24qSCXcNqASIiIiIiIoMxsCd9OINk7AHtGuipHnfnytjbmLEnIiIiIqLEwcCe9OGveZ7n11o10FO7xl5838419kRERERElDgY2JM+nK718w2b55lM2jfQEycILEHW2DNjT0RERERECYiBPelDBPYN59gDHmvdmbEnIiIiIiKKFAN70ofDz7g7wJ1Z1yxjzzX2RERERETUdDGwJ304/ayxB9wBuGbN85ixJyIiIiKipouBPenDEaAUX3TK16oU36Fyjj0z9kRERERElIAY2JM+/DXPA3Ronsc59kRERERE1HQxsCd9+JtjD7gz65o3zwuyxj7JlbG3M2NPRERERESJg4E9ac/pBCSnfN3XGnu9MvaWYM3zXBl9G9fYExERERFR4mBgT9oT2XogSs3z1Jbii4w9A3siIiIiIkocDOxJe2J9PeC7FF/r5nmqx92JjD1L8YmIiIiIKHEwsCftOTwz9tFonqd23J0rY++0AU6HNs9NRERERERkMAb2pD3PjL3PUnzRPE/rrvjBxt15BP7M2hMRERERUYJgYE/aExl7kxkw+/gTs2i8xt4R4hp7gOvsiYiIiIgoYTCwJ+2J5nm+yvABHZvnBVljbzYDFldWnxl7IiIiIiJKEAzsSXuiFN9X4zzP2zWfYx8kY++5jVbLAIiIiIiIiAzGwJ6053AF9maL7+8bNcce8AjsmbEnIiIiIqLEwMCetBe0FF+v5nkqAntl5B3X2BMRERERUWJgYE/aE83z/Jbi67XGXk0pvquBHjP2RERERESUIBjYk/bEjPioNc8Ta+yZsSciIiIioqaHgT1pTynF97fGXsPmeZLkMe5OzRp7ZuyJiIiIiCixMLAn7akuxddgjb1n1p8ZeyIiIiIiaoIY2JP2otk8z+4RoHONPRERERERNUEM7El7Yo29Jcn397Vsnud5csCSHHx7ZuyJiIiIiCjBMLAn7TmCZex1COwtKYDJFHx7ZY49A3siIiIiIkoMDOxJe0opvr+MvYbN80IZdee5HQN7IiIiIiJKEAzsSXvRbJ4Xyqg7ALC61tjbuMaeiIiIiIgSAwN70p4yx95Pxl5pnseMPRERERERUaQY2JP2nNEcdycCexWN8wBm7ImIiIiIKOEwsCftOYKssde0eZ4oxWfGnoiIiIiImiYG9qQ9p12+9Ns8T49SfK6xJyIiIiKipomBPWlPBPZ+S/Fdgb2mzfOYsSciIiIioqaJgT1pT+0ce00y9q7HsKhcY8/AnoiIiIiIEgwDe9Je0Dn2Bmbsra7tbAzsiYiIiIgoMTCwJ+05RCm+iuZ5khTZc4W6xj7JtcbezjX2RERERESUGBjYk/acQUrxPcvmRdl+uJixJyIiIiKiJo6BPWlPbfM8IPJy/FDn2CsZewb2RERERESUGBjYk/aU5nkW39/3LJuPtIGeUoofasaepfhERERERJQYGNiT9pQ59n4y9mYLYHIF/ZFm7JVSfLVr7NkVn4iIiIiIEgsDe9KeyNj7K8UHvBvoRUJk7C0qA3urqxSfGXsiIiIiIkoQDOxJe8Ey9oB7nX20S/HFdpIj8sZ9REREREREMYCBPWlPCez9rLEHtJtlH/K4O48TACzHJyIiIiKiBMDAnrQXSil+xBn7EMfdeW7HkXdERERERJQAGNiT9oLNsQd0yNirHHdnNrvX49u5zp6IiIiIiOIfA3vSnjLHPsn/Nlo1z3OEuMYe8Bh5x4w9ERERERHFPwb2pD2HEc3zVK6xB4AkV2d8ZuyJiIiIiCgBMLAn7Sml+AEy9pqV4oe4xh5gxp6IiIiIiBIKA3vSXkjN8yIN7F0Zf4vKNfYAM/ZERERERJRQGNiT9pwO+VJVxj7KXfEBZuyJiIiIiCihMLAn7TlDyNhHHNiH0TyPGXsiIiIiIkogDOxJe44Q1thrNsc+lOZ5Gi0DICIiIiIiigEM7El70Zxj7wijK77VlbG3MWNPRERERETxz9DA/sUXX0Tfvn2RnZ2N7OxsFBUV4ZtvvlG+X1tbi6lTp6JFixbIzMzExIkTUVpa6vUYu3btwrhx45Ceno5WrVrhzjvvhN1u99pmyZIlGDhwIFJSUtClSxfMnj07Gj9e0yXW2AecYy8y9pE2zwtn3J2rbN/ONfZERERERBT/DA3s27Rpg4cffhirVq3CypUrceaZZ+Kcc87Bhg0bAAC33norvvzyS8ydOxfff/899u3bh/POO0+5v8PhwLhx41BfX4+ffvoJb731FmbPno3p06cr22zfvh3jxo3DsGHDsGbNGkybNg1TpkzBggULov7zNhkONRl7DdbYS1KYzfOYsSciIiIiosQRIKWqv/Hjx3t9/cADD+DFF1/Ezz//jDZt2uD111/HnDlzcOaZZwIA3nzzTfTs2RM///wzTj75ZCxcuBB//PEHFi1ahPz8fPTv3x/33Xcf7r77bsyYMQPJycl46aWX0LFjRzzxxBMAgJ49e+LHH3/Ek08+iVGjRkX9Z24S1Myx16J5nud9mbEnIiIiIqImytDA3pPD4cDcuXNx9OhRFBUVYdWqVbDZbBg+fLiyTY8ePdCuXTssX74cJ598MpYvX44+ffogPz9f2WbUqFG44YYbsGHDBgwYMADLly/3egyxzbRp0/zuS11dHerq3CXilZWVAACbzQabzabRT6wPsX9G7meSwwYTALsESH72w2xKggWAo74GznD3te4oRE2ATTIDKh/HbEmWn7vuaPjPHQWxcCxJOzyeiYXHM7HweCYWHs/EwuOZOHgsQxfK78rwwH7dunUoKipCbW0tMjMz8emnn6JXr15Ys2YNkpOTkZub67V9fn4+SkpKAAAlJSVeQb34vvheoG0qKytRU1ODtLS0Rvv00EMPYebMmY1uX7hwIdLT08P+WaOpuLjYsOcedewoUgEs/ekXVKaX+Nymx/6d6A5g17bN+H3evLCeJ9lWiTGu6/MWfAuYTKru12PfXnQHsHPLn1hXF95zR5ORx5K0x+OZWHg8EwuPZ2Lh8UwsPJ6Jg8dSvWPHjqne1vDAvnv37lizZg0qKirw0Ucf4corr8T3339v6D7961//wm233aZ8XVlZibZt22LkyJHIzs42cM+Cs9lsKC4uxogRI2C1BljjrqOkTbcCduC0M4YBeT18bmP+8U+g5HO0P64AbcaODe+JKvYA6wHJkoKx48apvpv5xz+B0i/Q4bh8tA33uaMgFo4laYfHM7HweCYWHs/EwuOZWHg8EwePZehE5bgahgf2ycnJ6NKlCwBg0KBBWLFiBZ5++mlcdNFFqK+vR3l5uVfWvrS0FAUFBQCAgoIC/Prrr16PJ7rme27TsJN+aWkpsrOzfWbrASAlJQUpKY3XbFut1rj5IzR0Xx3yVAJrcirgbx+S5d+9WbLDHPZ+yt33TUmpof2sKXLVhdlpi+C5oyee/u4oOB7PxMLjmVh4PBMLj2di4fFMHDyW6oXye4q5OfZOpxN1dXUYNGgQrFYrvv32W+V7mzZtwq5du1BUVAQAKCoqwrp163DgwAFlm+LiYmRnZ6NXr17KNp6PIbYRj0E6iFrzPDHqLjm0+ynN89gVn4iIiIiI4p+hGft//etfGDNmDNq1a4eqqirMmTMHS5YswYIFC5CTk4PJkyfjtttuQ/PmzZGdnY2bb74ZRUVFOPnkkwEAI0eORK9evXD55Zfj0UcfRUlJCe69915MnTpVybhff/31eO6553DXXXfh6quvxnfffYcPP/wQX3/9tZE/emIT4+4sgcbdiTn2EQT24Yy6AzzG3bErPhERERERxT9DA/sDBw7giiuuwP79+5GTk4O+fftiwYIFGDFiBADgySefhNlsxsSJE1FXV4dRo0bhhRdeUO5vsVjw1Vdf4YYbbkBRUREyMjJw5ZVXYtasWco2HTt2xNdff41bb70VTz/9NNq0aYPXXnuNo+705JRL8QPPsXcF9o46/9sEYxcZ+xBG3QEcd0dERERERAnF0MD+9ddfD/j91NRUPP/883j++ef9btO+fXvMC9JVfejQoVi9enVY+0ghcjoASPJ1NaX49kgC+0gz9izFJyIiIiKi+Bdza+wpzjk8Zi1aAgT2SsY+klL8eu/HUosZeyIiIiIiSiAM7ElbTo/APlApvhbN85ixJyIiIiIiYmBPGhPr64EoNM/jGnsiIiIiIiIG9qQth0dgH2iNvSbN85ixJyIiIiIiYmBP2hKl+CYLYDL5305pnhfJHHvXfUOeY69B4z4iIiIiIqIYwcCetKVmhj1gbMY+yZWxt9cAkhT+8xMREREREcUABvakLTUz7AGNm+eFuMbe6joRIDm9u/gTERERERHFIQb2pC0R2AcadQdo1DxPjLsLtXlemsdjcJ09ERERERHFNwb2pC2RAQ/UOA/QuBQ/1MA+BYBr/b+NnfGJiIiIiCi+MbAnbYnmeWpL8Z12wOkM77mUcXchrrE3mTxG3jFjT0RERERE8Y2BPWnL6ZAv1ZbiA+Fn7cNtnge419kzY09ERERERHGOgT1pyxFixh4Iv4FeuOPuAO/O+ERERERERHGMgT1pyxniGnsg/AZ6zNgTERERERExsCeNqZ1jbzK5s/phl+KLNfYhNs8DPNbYM7AnIiIiIqL4xsCetCXW2AfL2APugNxuwBp7BvZERERERJQgGNiTtpwqM/aAx8i7cEvxxRz7MNbYW11r7G1cY09ERERERPGNgT1pS+0ce8CdsQ87sGfGnoiIiIiIiIE9actply/VBPYi0x5287wI1tgzY09ERERERAmCgT1pSwT2IZXic409ERERERFRuBjYk7bUzrEHIm+e54gkYy/G3TFjT0RERERE8Y2BPWlL7Rx7QIPmeZGMu3OV4jNjT0REREREcY6BPWnLIUrxY7x5npWl+ESUwOz1wJr3gIq9Ru8JERERRQEDe9KWM4RSfCOb54mTATYG9kSUgDbNAz67Hlj0H6P3hIiIiKKAgT1pK1rN8yTJHdhbIgjs7VxjT0QJqPqAfFlVYux+EBERUVQwsCdtKc3zLMG3jaR5nsMGQPJ+nFAo4+6YsSeiBCROWtZXG7sfREREFBUM7Elbyhz7UDL2YZTie66N57g7IiJv4qRl/VFj94OIiIiigoE9aUtk7NWU4keSsfe8T0QZe5biE1ECUjL2DOyJiIiaAgb2pK2wMva20J9HrMu3JAMmU+j3Z8aeiBKZkrFnKT4REVFTwMCetOUMZ9xdBBn7cMrwAWbsiSixeWbsJcnYfSEiIiLdMbAnbSnN81QE9sq4u3ACezHDPowyfIAZeyJKbOKkpdMeXh8TIiIiiisM7Elb4cyxD6t5XoQZewb2RJTIPKuRuM6eiIgo4TGwJ22FU4ofSfM8cXIgVFZXYM9xd0SUiDxPWtZVGbcfREREFBUM7Elbjig1z1NK8cPN2LvW2Nu5xp6IEhAz9kRERE0KA3vSljOENfaaNM8Lc409M/ZElMg8M/YM7ImIiBIeA3vSVihz7CNpnueItCt+huu5a9xVBkREicLzpCVH3hERESU8BvakLWWOfQhd8SNqnhfmGvv0Fu7lAlX7wnsMIqJYZY+DUvzKfcCun43eCyIiooTAwJ60pTTPU5Gxj6h5XoRr7M1mILetfL18V3iPQUQUq2xxUIo/5yLgjVHAwb+M3hMiIqK4x8CetBXOHPuwmudFuMYeAHLbyZcM7Iko0diOua/HYin+kZ1Aye/y9Yrdxu4LERFRAmBgT9oKZY69Fs3zLAzsiYgascf4Gvut37qvx2pFARERURxhYE/acjrkSzVz7CNpnqeU4jOwJyLyIkmxP+5uCwN7IiIiLTGwJ205QsjYa9I8L8w19gCQ216+ZGBPRInEUQ9Acn8da4GzvR7Y9r3761isKCAiIoozDOxJW+HMsTc8Y78z/McgIoo1ntl6IPYC5z2/AvVV7q9j7cQDERFRHGJgT9oSM+FDKcUPp3meyPJrEdhX7OUseyJKHJ7r64HYC5y3LPL+Otb2j4iIKA4xsCdtRa15XoTj7gAgs0DeT8nBWfZElDgaZexjLHAWgX32cfJlrO0fERFRHGJgT9oKZY690jwvkjX2EWTsOcueiBJRo4x9DJXiV5UAJesAmICe4+XbYmn/iIiI4hQDe9JWWHPsDcrYA+yMT0SJx3OGPRBbGfGt38mXhf3dDUxjaf+IiIjiFAN70pbI2IfaPE+SAm/bkMjyi5MD4WJgT0SJxtYgY18XQxlxUYbfZTiQnCFfZ2BPREQUMQb2pC2RsQ+lFB8S4HSE9jzM2BMR+WaP0TX2Toc7Y+8V2MfQiQciIqI4xcCetKVk7ENongeEXo6vxRp7gLPsiSjxiIx9ao58GSuB877VQM0RICUHOG4wkJwp3x4rJx6IiIjiGAN70lY4zfOA0GfZa56x5yx7IkoQ4vUxI0++jJXAWZThdzpDHonKUnwiIiLNMLAnbSnN8yzBtzUnATC57hdiZ3yR4U/SaI09Z9kTUaIQ4+7SW8qXTlt400e05rm+HmBgT0REpCEG9qStUObYm0zeDfRCoZTiR5ix5yx7Iko0Ssa+pfs2o8vxj5UBe1fJ17ucJV8qpfgxslSAiIgojjGwJ+1IUmil+ABgcQX2ItOvllal+JxlT0SJRmTskzPdr7FGZ8W3LQEkJ5DXE8hpI9/GjD0REZFmGNiTdjw726sZdwe4S+lDbp7nKiuNtHkewM74RJRYRGBvTY2dzvNbvpUvRbYecO9brCwVICIiimMM7Ek7To+su9rAXjTQC7d5noWBPRGRFzHuLiktNjrPS1Lj9fWAO7AHjD/xQEREFOcY2JN2PMvpVZfii4x9iNkarcbdAQzsiSixiHF31rTYyNiXbgCqSwBrOtCuyH27xRo7SwWIiIjiHAN70o7To6u8muZ5QHjN8yRJuzX2AGfZE1FiERl7axqQEgMZe5Gt73CavDzAE9fZExERaYKBPWnHK7BXMe4O8MjYh9A8z2kHIMnXNc3Yc5Y9ESUAm8eJz1gInEvXy5ftT2n8vVhYKkBERJQAGNiTdpQZ9knyKDs1RGAeSvM8ka33vH8kOMueiBKJZ8Y+FkbKidfslKzG34uFpQJEREQJgIE9aSeUGfZCOM3zPLfVonkeZ9kTUSKJtYy90hPFx9KpWNg/IiKiBMDAnrQjxt2pbZwHhNc8T+mInyzPoY8UZ9kTUSKxHZMvvZrnxUBg7+tEbCzsHxERUQJgYE/a8SzFVyuc5nmBsj/hYmd8IkoUdh8Z+7oqA/cnwBSTWFgqQERElAAY2JN2nGEE9mFl7EX2J1n9fYJhYE9EiUIZd5caG83pAk0xYcaeiEh/VSXuylpKWAzsSTsiYx9KKb7SPC+MUnxm7ImIGlOa56XHRmAvXt+TfJyMZWBPRKSv7UuBJ7oDxdON3hPSGQN70o44ExhOxj6sUnwNGucJnGVPRIki5prnqcnYsxSfiEgX25bIl6vfAewhJNIo7jCwJ+04w8jYh1OK79BzjT1n2RNRnIu5cXeu13dfy6dioaKAiCiRHd4iX9ZWADuWGrsvpKuwAvvdu3djz549yte//vorpk2bhldeeUWzHaM4FPXmeTqssecseyKKd3GZsWdgT0Ski8Nb3dc3fmHcfpDuwgrs//73v2Px4sUAgJKSEowYMQK//vor7rnnHsyaNUvTHaQ4Eskce6PX2HOWPRElAklqkLGPgcBZWWMfaNwdS/GJiDQnSUCZR2D/59dsopfAwgrs169fjxNPPBEA8OGHH+L444/HTz/9hP/973+YPXu2lvtH8URkui1hZOzD6Yqv5Rp7zrInokTgqAckp3w9KTVGSvHFydhA4+6YsSci0lzVfsB2DDBZgNQc4OhBYNfPRu8V6SSswN5msyElRX6DXrRoEf72t78BAHr06IH9+/drt3cUX5yuwD6cjH04pfgWDQN7gJ3xiSj+2Wrc170y9gYF9k6nR8aepfhERFEl1tc36wB0HydfZzl+wgorsO/duzdeeuklLF26FMXFxRg9ejQAYN++fWjRooWmO0hxJGpz7ANkfyLBwJ6I4p14fTSZ5dfXFIMz4p6v7T6b5zGwJyLSjQjsW3QBeo6Xr2/8Ui7Rp4QTVmD/yCOP4OWXX8bQoUNxySWXoF+/fgCAL774QinRpyYoklL8sJrnabjGHmBgT0TxT2Tsk9IAk8kdODvqjRlzJE40AH4y9jGwVICIKFGJxnktOgOdz5Rfcyv3Ant/M3a/SBchRGBuQ4cOxaFDh1BZWYlmzZopt1977bVIT0/XbOcozkSteZ744Kp1xp6z7IkozolA2uoKoq0Z7u/Zjmo7TUQN5bXd5HsUKjP2RET6UTL2neX3ha4jgQ2fABs/B9oMMnbfSHNhZexrampQV1enBPU7d+7EU089hU2bNqFVq1aa7iDFEbHGPpQ59uE0zxMZqeSMwNuFirPsiSjeeWbsATmQFydQjQiePZdOmUyNv8/AnohIP56l+ED0yvFryoHvHwPKtqnb3mF3j2qlsIUV2J9zzjl4++23AQDl5eU46aST8MQTT2DChAl48cUXNd1BiiPhzLEPp3mezWOUk5Y4y56I4l3DjD1gbPAcbIqJKMW313AEExGRlhw24MgO+boI7LuOlJdFlW0DSjfo99zr5gKL7we+e0Dd9h9NAp7oDlSyCXskwgrsf/vtN5x++ukAgI8++gj5+fnYuXMn3n77bTzzzDOa7iDFEaUrvs7N82zH5Eurxss+Mgvk/eEseyKKVw0z9oCx69iDTTHxrLxi1p6ISDvlu+TP5klpQFahfFtKJtD5LPm6nt3xjx2WL/evDb6tvR7YNB+oLQe2LdFvn5qAsAL7Y8eOISsrCwCwcOFCnHfeeTCbzTj55JOxcyfLmJsskbEPpxQ/lIx9vU6BvdkM5HCWPRHFMaWiyUfGvs7AwN5fs9OkFHm+MsDAnohIS57r680eIZ9nOb5exInksq3uz+3+HNrk7tO1d6V++9QEhBXYd+nSBZ999hl2796NBQsWYOTIkQCAAwcOIDs7W9MdpDgSyRx7cVJADSVjr3EpPsDO+EQU35Q17Z6BvYEj7xxBSvFNJmP3j4goUXkG9p66j5araw/8ARzaos9zi9dzyQkc3Bh425L17ut7V+mzP01EWIH99OnTcccdd6BDhw448cQTUVRUBEDO3g8YMEDTHaQ4Is62hTPuzhHOGnsdJjAwsCeieObr9dHQNfYezfP8UfaPI++IiDTTsHGekNYM6HiGfF2vcnzPLL1n4O5Lqcf3S9aziV4Ewgrszz//fOzatQsrV67EggULlNvPOussPPnkk5rtHMUZRwRr7GOheR7AwJ6I4pvP5nkxsMZeVWDPjD0RkWaUGfZdGn9PKcfXK7D3eL8J1qSvZJ37utPm/TWFJKzAHgAKCgowYMAA7Nu3D3v27AEAnHjiiejRo4dmO0dxJlpz7PVqngdwlj0RxTefzfNioCu+v+Z5AAN7IiI9BArse5wNwATsW63PZ17P1/PSABl7SXIH8qLBH8vxwxZWYO90OjFr1izk5OSgffv2aN++PXJzc3HffffB6XRqvY8ULyKZYx9Sxt4V2CfrENhnt5Yvq0q0f2wiIr3F27g7wNiKAiKiRFR/DKiUE68+A/vMPKD9KfL19Z9o//w2j1L80vVyAO9L1X6gpkxuotrvYvk2BvZhCyuwv+eee/Dcc8/h4YcfxurVq7F69Wo8+OCDePbZZ/Hvf/9b632keBFJKX5IzfN0LMXPyJMvjx7Q/rGJiPQWMGNvQODsCNIVH2DGnohIa2Xb5Mu0ZkB6c9/biEB6xevuz/Ba8Xw9r60AKvb43k6sv2/ZFehwqnydnfHDFlZg/9Zbb+G1117DDTfcgL59+6Jv37648cYb8eqrr2L27Nka7yLFDaUUX+/meTqW4ovAvrZCnqtJRBRPYnaNfbL/bRjYExFpy1/jPE99LgDSWwAVu4A/v9L2+ZX3G5N84W+dfamrDL+gD1A4UL5etg04Vqbt/jQRYQX2ZWVlPtfS9+jRA2VlPBBNVjhz7D3X2Psr02lIz4x9aq77xMSxQ9o/PhGRnsSJT8+MfYqB4+R8jd9riF3xiYi0pSawt6YBg6+Wr//8orbPL7ri5/eWL0v9NMQT6+vzj5crC5q7RvPt+03b/Wkiwgrs+/Xrh+eee67R7c899xz69u0b8U5RnHJGUIoPqGugJ0n6ZuzNZiC9pXy9muX4RBRnbDG6xt4SKGPPOfZERJpSGud1DrzdCVPkpte7f9Z2bbt4PW97onzpL2MvSvELjpcvjxskX+7hOvtwhBCBuT366KMYN24cFi1apMywX758OXbv3o158+ZpuoMURyJpngfIHwADNVgC3Nl6QJ/AHpAbilSXAEeZsSeiOGP3Ncc+FkrxucaeiChq1GTsASCrADh+IvD7+3LWfuJrkT+30wnYRGB/ErDyDd+z7OuPAWWuExD5feTL4wYB6z5kA70whZWxP+OMM/DXX3/h3HPPRXl5OcrLy3Heeedhw4YNeOedd7TeR4oXjjDW2HuOQFLTQM8rsNehFB9gAz0iil82H6XvRgbODs6xJyKKOrWBPQCcfL18ueFToHJf5M9t9/isLjL2ZVvd5fnCgY2A5JQ/d2fly7e1GSxf7l2lfokuKcKeY19YWIgHHngAH3/8MT7++GPcf//9OHLkCF5//XXVj/HQQw/hhBNOQFZWFlq1aoUJEyZg06ZNXtvU1tZi6tSpaNGiBTIzMzFx4kSUlpZ6bbNr1y6MGzcO6enpaNWqFe68807Y7d7dHZcsWYKBAwciJSUFXbp0YZM/PYQzx95sdp8IUNNAT5ThW1IAsyW0/VMro5V8efSgPo9PRKQXu48eJLFQis9xd0RE0XGsTB4hBwDNOwXfvnAA0O4UufJ2hQYZe+W9xgQ06ygvcZWcwMGN3tuVeqyvF/KPl+OIY4eA8p2R70sTE3Zgr4Xvv/8eU6dOxc8//4zi4mLYbDaMHDkSR4+6P3zceuut+PLLLzF37lx8//332LdvH8477zzl+w6HA+PGjUN9fT1++uknvPXWW5g9ezamT5+ubLN9+3aMGzcOw4YNw5o1azBt2jRMmTIFCxYsiOrPm/CcDvnSEuIKD0sIs+z1bJwnZHCNPRHFKZ8ZeyNL8UNpnseMPRFRxMT6+qxC9+trMEU3ypcr32icWQ+VeK9JzgBMJo8Geg3W2Zd4dMQXrKnu9fYsxw+ZoYH9/PnzMWnSJPTu3Rv9+vXD7NmzsWvXLqxaJR/IiooKvP766/jvf/+LM888E4MGDcKbb76Jn376CT///DMAYOHChfjjjz/w7rvvon///hgzZgzuu+8+PP/886ivl5uxvfTSS+jYsSOeeOIJ9OzZEzfddBPOP/98PPnkk4b97AnJEUbGHnCvyVfTPE9k7NW+UIUjU2TsucaeiOKMkrGPkVJ8MTY0YPM8BvZERJopU9k4z1P3sUBuO6DmCPD7B5E9f32Dz+oicG+4zl5pnNfH+/bjRDk+O+OHKqzmeXqpqKgAADRv3hwAsGrVKthsNgwfPlzZpkePHmjXrh2WL1+Ok08+GcuXL0efPn2Qn5+vbDNq1CjccMMN2LBhAwYMGIDly5d7PYbYZtq0aT73o66uDnV17uxxZWUlAMBms8FmU7EO3EBi/4zYT4ujHmYAdskEKYTnT7IkwwTAVnsUCHI/U00lkgBISamw6/QzmlKbIwmAs7oUDgOPt5HHkrTH45lYYvV4JtlqYAJgN1ndr8PmVFgBSHXVur1u+mOxHYMZgMNshdPPc5ssqfLrel1V1PdPiNXjSeHh8UwsPJ6hMR/4CxYAjmad/L7u+rzf4GtgWfRvSD+/AHvfS+VsexhMNRXya7o1HXabDaaWPeXP1SXr3Meyvg5Jpevlz/8tenh9/jcV9Je3373C0M/hsSKUv/uQAnvPEnhfysvLQ3k4L06nE9OmTcOpp56K44+XSzBKSkqQnJyM3Nxcr23z8/NRUlKibOMZ1Ivvi+8F2qayshI1NTVIS/Mu637ooYcwc+bMRvu4cOFCpKfr1IldY8XFxVF/zlMOlCAPwJrf12PvnizV9xthcyIdwE9Ll6A8Y3fAbfMqf8cpACpqbPhepwkMrSp3oAhA5f6tuj1HKIw4lqQfHs/EEmvHc0RlGdIBLPvlN5SvLwcAWO3VGAvA5KjDN19/AckUvXP6J+7djdYA1m38CzsP+n49bV69CacDOFp+AN8a/Joba8eTIsPjmVh4PNUZvH0pjgOw8UA9tobwmprkaIWR5lRYD/2FFR88goPZ4Y0wz6tch1MAVNY6sGTePGQfK8MwAPa9a1C8cCFgMmHZ1+9hRH01HKYkfLNiMyTTduX+mbVVOAuAc+/qqL9nxaJjx9QvjQjpN5WTkxP0+1dccUUoD6mYOnUq1q9fjx9//DGs+2vpX//6F2677Tbl68rKSrRt2xYjR45Edna2gXsWnM1mQ3FxMUaMGAGrNcSS+AhZ3n4BqAb6DzoB/XqOVX2/pJ0zgbJDOPWkwZDaFQXc1vSnBGwFslsUYOxY9c8Rkv3HAVsfR46lTr/nUMHIY0na4/FMLLF6PJM23QrYgFOGDgfyesg3OuqBdfL6yTFnnQGkBn4v15LlvbeACuD4/oPRu6+f19OStsDmB5BhhWGvubF6PCk8PJ6JhcczNEmvPQYA6HHq2ejedVRI9zWn/AaseBknS6vhGPvPsJ5ffFbPatFafk2310F6bCaSHccw8uTeWPjLHxjSLRf4AzC36okx4/7m/QCSE9K2B5FUV4kxgzo2LtVvYkTluBohBfZvvvlmyDujxk033YSvvvoKP/zwA9q0aaPcXlBQgPr6epSXl3tl7UtLS1FQUKBs8+uvv3o9nuia77lNw076paWlyM7ObpStB4CUlBSkpDTu4Gu1WuPmBcWQfZXk5nlJyalAKM/taqqUBEfw+0nyek1zcjrMev18uYUAANOxw7BaLHLnfgPF098dBcfjmVhi7ni6mudZUzPdr6dWq9z7xGmD1VkX2utzpJzya3ZSSrr/502XTzSY6o8Z/ruMueNJEeHxTCw8nipIkrLGPqlVj9Bf74uuB1a8AvO2b2E+shVo1SP0fXDK70PmlAz5s7rVCrTsBhz4A9YyefpZ0qE/AQCm1v18H9PjBgLblsBaugZoOzD0fUggofzNGxqxSJKEm266CZ9++im+++47dOzY0ev7gwYNgtVqxbfffqvctmnTJuzatQtFRXJmt6ioCOvWrcOBA+4O5sXFxcjOzkavXr2UbTwfQ2wjHoM0Es3meVYdm+elu7riO+1Abbl+z0NEpCVJ8j3uDjCuQZ2YdmJROe6Oc4uJiMJXtV/+rGyyAM3ah37/5p2AHuPk6z+G2WTcsyu+4BppZzrwh+vS1SG/4Hj4dNwg+ZKd8UNiaGA/depUvPvuu5gzZw6ysrJQUlKCkpIS1NTIH0xycnIwefJk3HbbbVi8eDFWrVqFq666CkVFRTj55JMBACNHjkSvXr1w+eWXY+3atViwYAHuvfdeTJ06Vcm6X3/99di2bRvuuusu/Pnnn3jhhRfw4Ycf4tZbbzXsZ09ITrt8Gep8+aQQxt2JTpt6jrtLSgZSc+XrnGVPRPHCYZNnBQONx8sZNfIulHF3kNwjTYmIKHSHt8iXzTq4E2ehOv12+XLdXODIjtDvr3TFz3Tf5hp5Zypd77p0Bfb5/gJ7dsYPh6GB/YsvvoiKigoMHToUrVu3Vv598IF7zMKTTz6Js88+GxMnTsSQIUNQUFCATz75RPm+xWLBV199BYvFgqKiIlx22WW44oorMGvWLGWbjh074uuvv0ZxcTH69euHJ554Aq+99hpGjQpt3QkFITL2ob6QiDFIIWXsdQzsASAjT77kLHsiihd2j6A4VjL24nU9KcC4O6tHU1qOvCMiCp8I7Ft0Cf8xjhsIdD5TXmK77OnQ7y9exz1f2wvcGfsk+1GYKnZ53e5zHwDgwEagrir0fWiiDG0zKKkouUtNTcXzzz+P559/3u827du3x7wgXR+HDh2K1atXh7yPFAIlYx9iYC8y9qoCe1FmqvN0gow84PBmZuyJKH641tcDpsZz41NiOGNvNsvLq2xHXfuXF5VdIyJKOIfFDPsIAntAztpv/Q5Y/S5wxt1AVoH6+wYoxUfZVjTLde1jdhsgrZnvx8gqkL9fuQfYtwboeHrIP0JTZGxXMEosznAz9iGU4tv8rB/VWqbrgyUDeyKKF57r6xvOHzZsjb3I2AdYYw8Yt39ERIlEydh3juxx2p8KtD1ZTrr99Gxo9xWv456l+Jn5QHpLmCQn2hxZLt/mL1svtOE6+1AxsCftOMJcYx9S8zzxYqFj8zzAXYrPwJ6I4oU48ekrO270GvtAzfMABvZERFrQohQfkE8Oi7X2K98EjpWpv69YNpvsUV1rMimBfGH5Svm2YGPslAZ6K9U/dxPHwJ604wyzK34ozfOilbHPaCVfMrAnongRaKmS0V3xg2bsDTrxQESUKBx2d7O7SAN7AOg6Qg6+bUeBX15Sf796P0k4Vzl+krPO62u/RGC/hxl7tRjYk3bEGvuEaJ7nGnlXzcCeiOKEyI5bfWXsjWqepzawZ8aeiCgi5Tvlz+LWdCCrdeSP55m1/+UloLZS3f2UNfaZ3rc3DOSDZexbyWPLUbVPXfKPGNiThpRS/BB7MsZi87xMZuyJKM4opfg+TnwakRF3OtwnfAM1zwMY2BMRRUqUy2e0lJuSaqHn34AWXYHaCmDlG+ruo4y7a5ix761clawZQLOOgR8nJdt9vY7VXGowsCftKKX4IQb2sdg8T1ljz3F3RBQnYi1j7/marjpjzw9vRERhURqoatiHymwBTrtVvr78effn8EB8jbsDgLzukFwxgtSqZ/CTD5Yk92PUqawWaOIY2JN2wp5jH0LzPOXFIlrN8w7p+zxERFoJ2DzPiMC+1n09aPM8UVHAjD0RUViU5FeQCqlQ9b0QyGkrJ7tWvxt8e3+l+EkpcvYfgNSqN1Rh/5WQMLAnbUgSIDnk6wnRPM8V2NdXu0uKiIhimZKxD1CKX1cVxf1xvaabLHLmJRCW4hMRRUbpQ6XxclWLFTj1H/L1ZU8DTqe6/fAxwcrZQZ5HL3VQOZc+JUu+jOZ7VxxjYE/aEOsogeAf4BoKqXlelNbYp2S5s15cZ09E8UDVuLsoBs5qG+cBDOyJiCKlZ/JrwGXyZcVuoCbI6DulK37jz+rOoffgh27TIfU8R93zpoiT0szYq8HAnrQhyvABnZvnRakrvsnEWfZEFF8Cfagzco29qsBenHhgVoaIKCx6BvbWNCDZlT2vrfC/ndPpkbHPbPz95Awcyegif85WQzTQ4xp7VRjYkzacnoF9qGvsY7AUH2BgT0TxJWApvpGBvYr1nszYExFFJtBkFC2kuoLsQIG9zWP5qo9S/JBxjX1IGNiTNpwO93W9mudJUsB1O5oTgX01O+MTURyItXF3IrAXy60CYWBPRBQZvZNfqTnyZaDAXnkNN6k7qRsM19iHhIE9aUMpxTfJozFCobZ5nr0WgCRfj0bGPpMZeyKKIzE37s61P8zYExHpz65zHyoR2Acqi/fsiK+23D4QrrEPCQN70ka4M+wB9c3zPGdn6lVm5Iml+EQUTwJm7A0InJXmeWoy9iy3JCKKSCxk7LWurGXGPiQM7Ekb4c6wB9Q3zxMvFpbk0Dvvh4OBPRHFk0AZe/HhyF4DOOyNv6/L/nCNPRFR1OjdYDpFxRr7AB3xwyIa9rGxqioM7EkbYtxdqI3zAPXN86I16k7IaCVfco09EcUDNRl7ALBFKXhmYE9EFD2xkLFXSvGZsTcCA3vShgjsw8mkq22eJz7wRS2wbylfHj0UnecjIoqE8qHORyBtSXYvlYpW8BxK8zzx4Y2l+ERE4YlaYB9ojX2AUXfh4Br7kDCwJ22IUvxwMvZqm+dFc9QdAGS6MvZHmbEnojgQqHGSyRT9rLgjzIy9JOm3T0REiSoWxt1pnYRjxj4kDOxJGxE1zxNr7G2Bt1PWDkUrY+9aY3+sLHprUomIwmUL0oU+2g3q7KE0z3MF9k578OotIiJqLBFL8dlYNSQM7EkbjghK8cWHPkeMZezTWwAwAZCAmrLoPCcFt/4TYMNnRu8FUeyxByjFB6KfsQ9l3J3V40Mg19kTEYVO7wSYmnF3Nq1L8ZmxDwUDe9JGNJvnadVpMxizxRXcgw30YkVdFfDJNcDcK4Gdy43eG6LYomTs/Zz8jHpg78q8i+VWgViS3CcAmJkhIgqdMhnFyIy9xl3xGdiHhIE9acMZwbg7tc3zbFFungd4rLPnyLuYcKzMfRLp69uCL98gakoCjbsD3BmUaH1AEvtjURHYA+yMT0QUCd3H3YUS2LMU3wgM7EkbohTfbAn9vrHaPA/w6IzPwD4meAYkB/4Afn7RuH0hijXBGicpH5Ci3TyPgT0Rke5iYo29COw1LsV31AePE4iBPWnEGUFXfJHNkRyA0+F/O73PRPqSwYx9TFECe5N8seRhoGKPYbtDFFOCZuyjXYofamDPzAwRUdhsASajaEFZY18FOJ2+t9G6K77nCQKOvAuKgT1pQ5ljH864O4+OyYHOxun9guWL6IzPNfaxQTRsad0XaFckL8+Y/09j94koFkiS++RnzKyxF2v+mbEnItKd7hl717g7SP4b6Gldim9Jcn/uD9S0jwAwsCetRDLH3poBJQMbKFNjRGCf6Qrsjx6K3nOSfyJjn5oDjHsCMFmAjV8Cfy00dr+IjOawAZIrg+LvQ13Ux925+qZwjT0Rkb4cNnf1rJpJJOFISnE/tr9yfJvGpfiej8VqrqAY2JM2nBGssTebgRTXWcDaAGfjtC7vUUNk7I8yYx8TxBtJSjaQ3xsoulH+et4dQP0x4/aLyGhi1B0QILCP4XF3AD+8ERGFy+b5HqDj5+RgI++07ooPsDN+CBjYkzYcEXTFB9Q15DCkeR7X2McU8aIuTgSd8U8g+zigfCew9Anj9ovIaGLUHUyAJdn3NtEO7B0hjLsDmLEnIgqX3eM9QO1rbjiCfV7XuhQfAFLERBee9A2GgT1pI5I59oDHC0W5/20MaZ4n1tgzsI8JSmDvOnubkgmMfli+vuxp4NBmY/aLyGh2jxOfJpPvbVKiXYrPNfZERFHh+RnZ33uAFpQK22CBvYal+OI5ucY+KAb2pA1ljn1SePdPDfJCARjUPM9j3J0kRe95yTfxoq40cAHQczzQdaT8N7jgHmP2i9wq9gBbvzN6L5oem4qy96ivsQ933B2zMkREIYlWVavajL2Wn9W5TEs1BvakDWWOfbiBvZpSfNfZSC3X7QQjMvaOOp4pjAUNM/aAfGZ61EPy9c0Lgcp90d8vcvvkWuCdc4H9a43ek6bFruJDnVHj7lQ3zxMf3pixJyIKiZKx1/kzsvJ5PUpd8QGusQ8BA3vSRiRz7IHQAvtoZuyT090fNtkZ33i+AnsAaNlFHn8HCfj9w6jvFnko3y1fHtxk7H40Naoy9rE+x56l+EREYRHvAbpn7ANU2Dod7pPMmpbic429WgzsSRuRzLEHYrd5HsBZ9rHEsyt+Q/0uli/Xvs9lE0YSpXKVe43dj6ZGTQ+SaJczOkRgr7YrPgN7IqKwiM/Ieo26EwJ9Xrd5TCdiV3xDMLAnbWhVih+o3N2IjD3gMfKODfQM17ArvqdeE+SS34MbgZLfo7pb5EEJ7LkkIqrsKrI1ho27C7UUn1kZIqKQRL0U30dgL95bTGZtTzAkuwL7egb2wTCwJ20opfh6rrE3KGOfyZF3McNX8zwhLRfoPka+vvb9qO0SebDXu0ecVTBjH1VqsjUsxSciSkzRbp5XFyCwT87UtjM/M/aqMbAnbUQ6xz7Y+AwAqDcqY+/RGZ+M5W+NvdDvEvly3Vx3FQlFj2emlaX40aUqY+/6f2M7Jq+F1H2fQm2ex8CeiCgsahqoaiFFRcZe68/pXGOvGgN70oZT5674kmRgKT4z9jEjWGDf5SwgvaV8rDhyLfq8AnuW4kdVKBl7IDrBc8gZe5biExGFJRbG3enRER9wf+bje0NQDOxJG3o3z7PXAXA1RGPzvKbJXu/OSvpaYw/If399LpCvr30vOvtFbp5n048ekI8ZRYeajH1SCmCyyNf1Duwlic3ziIiiRU0DVS0EGndn0ymwFyd9WYofFAN70oZD5zX2np02o77GXjTP47g7Q3m+oPvL2ANAv4vky03zAi/tIO01PJtetd+Y/WiK1Iy7M5miNyveaQckp2ufktXdh4E9EVF4lHF3ejfPC7B0VreMves5GdgHxcCetKHZHHs/XfFFiZHZGn5VQLiUrvjM2BtKNM6zZgBmi//tWvcH8nrIGcw/Po/KrpFLwzddluNHj9r1lUrwrHNJo6ggAELI2Ge678seGURE6okEWDTH3TUcLaxbYM+MvVoM7Ekb4kOYJcKMve2oO/vvSbxgaTkXUy2usY8NgTriezKZvGfaU/Q0zLSygV70qF1fGa2suOcyjFCb5wHukk4iIgpOeQ+I0rg7ydH4fYRr7A3HwJ60oTTPi7ArPuBn3Y5BjfMAd1f82gp3MyiKvmCN8zz1uRCACdi5DDiyU9fdIg8N33QZ2EeP0jwvVgJ7V8bebAXMKj9qWJLdy7lYjk9EpF60mudZ092v03UNPq8rXfF1WmPvqOfn8CAY2JM2nBGOu7Mkuf/j1pY3/r5RM+wBIDXX/SLGdfbGESd8/DXO85RzHNBxiHz99w/12yfy1nAUDUvxo0dpnhekDDNamY9QG+cBrh4AXGdPRBQye5Qy9iaT/xHVemfsAY68C4KBPWkj0uZ5QOAGekZm7M1mrrOPBaFk7AH3TPu17zVeB0b6qG+4xp4Z+6iJuYy9COxVNs4TOPKOiCh0SgJM5zX2gP/P68qyWY0De7PF/fm/YZUAeWFgT9qIdI49ECSwj9KZSH9EOT4z9sYRL+ZqA/ue4+W/l7KtwN5V+u0XuYlgMaedfMmMffSozdhHrXleGBl7gBl7IqJwRDMB5q/htXhf0TqwB3jSVyUG9qSNSOfYA4ED+/oozef0RzTQ4yx746htniekZMrBPQBs+FSffSJvokQur5t8WcGMfdSEmrGvOaLv/ojA3hJqxp6BPRFRyKK5ZNXfyDu9SvEBd1KHnfEDYmBP2nBEOO4OcAf2vspsjCzFBzxK8dkZ3zBKKb7KwB4AWveTL6tLtd8fakycSW/ZXb6sLvU95YK0pzZjn99Hvtz5U3T2J+SMPbMyREQhs4X5mhsOJRFX7n17vU6l+IDHyDu+NwTCwJ60oZTiB5gvHoyqUnyDMvaZDOwNF0rzPCGZbwRRJU6+NOvgOsknAVUlRu5R06HMMA7yGtnlLPly18/6Zj4crnF3SSpH3QnM2BMRhc6QUvyGGXvXZy2tu+ID7s9+XGMfEAN70oYjwq74QOw2zwOYsY8FoTbPA9xneJn9iw7xe07JArJby9e5zj46RLYm2MnPFp2BZh3lSSbbl+q3P0rGnoE9EZHuolqKnytf+ht3xzX2hmFgT9qIdI494H98BuB+wUo2KrDnGnvDhRXY8wxvVIk39ZRMIPs4+To740eH2lJ8AOgyXL7cskjH/RHN88IN7PnhjYhItWiNuwP8f17Xqys+wDX2KjGwJ21EOsceUJmxN6p5nsjYsyu+YUJtngewFD/axO852TOwZ8Y+KtQ2zwM8Avti/UZBKs3zQg3sRVaGGXsiItViYdydnl3xucZeFQb2pA2H3mvsDS7F5xp744U67g5gKX60iTn2yZlAdqF8nRn76AglY9/hNLlbffku4PBWffeHpfhERPpyOj3eA4wcdxeFrvj8PBcQA3vShlPDrvgNXygA45vnea6xdzqN2YemLpyu+Czdii5xJp2l+NEXSsY+JRNoVyRf16scX2mexzn2RES6EmX4gMHj7nQsxU8Wn+e4tDIQBvakDb3n2BudsU9vKV9KjsbjPSg6wuqK73ojsB0DnA7t94m8KWfrPTP2LMXXncMmvzYB6ssw9V5nH3bGnlU2REQhEc1TAXUndyPl6/O60+Gxzl/PNfZ8bwiEgT1pw6FB8zxV4+4MCuyTkt1dQNlAL/okKbKu+ACz9npz2ACHa111CtfYR5XNI1uj9kOdGHu340fvD4VasXPcHRFRVIjklyUFMEchtPP1ed3zNVvXNfb8LBcIA3vShlKKr9Ma+3qDm+cBQKarMz7X2Uefvdb9NxZK87ykFHktMcAMoN4832yTM4EcV2BfVeI+8Uf6UAJ7k/pAulUvIKu1nGHZ9ZP2+yQy9iE3z2NgT0QUkmgvVxWf1z3L4sXJBZMl9BO6anCNvSoM7EkbWs6xr69qHAgYXYoPeKyzZ8Y+6pSg0RR6iRc740eHeLO1pMivAxl5gDlJLhGvLjV23xKd3eNDncmk7j4mkztrv+VbHfaJ4+6IiKIi2p+RxZJIe6274stzKZ7a96FQJDNjrwYDe9KGlnPsgcbNMYxungcAGa519hx5F32eZfihlpmxfCs6PGfYA3L1TlZr+TrL8fUlPliF2qhOz3X2jnADe467IyIKSShTUbSQkg3AFbyLz+vKqDudTi6IGIGf5QJiYE/aUJrnJYX/GEnJ7rONDcvxYyJj7yrF5xr76BN/D6E0zhPEfer5ZqArzxn2AkfeRYc9zBOfnYYCJjNw8E+gfLfG+8Rxd0REURHtz8hms/uzlfh8pmdHfIBJGpUY2JM2RCm+OYLAHvC9bgdwv2jpdSZQjQzOsjdMOI3zBJbiR0e9j2PEzvjREW7GPq0Z0OYE+fpWjcvx7Rx3R0QUFUZUtTYcUa3nDHuAa+xVYmBP2tBijj3gv4FeLJTiZzKwN4w40RNOYM+zvNGhZOw93tQ5yz46ws3YA/qV4yvN85JDu59nKb7Tqe0+ERElIkMCe5GxL5cvRcCtx6g7wP3e4Kh393ChRhjYU+ScTkByfQCLpHke4Duwl6QYKcVnYG8YEZSH0hFf4Fne6Kj3VYrPkXdREW7GHnA30Nv2vbvySgtK87wQ90lZbiO5PzASEZF/IrCPxgx7oeHndZvepfgeiR1WYPrFwJ4i5/ToYK9VKb5nYO+od584MLR5HtfYG0aTUnxm7HXVsHkewDX20RJJxr71ACC9hVwVs2eFdvsUbvM8a6q8RADgNAUiIjWMLMWvi1IpvtniTu41XK5LCgb2FDmnR5Yn0ox9w2YcgPssIGBwxp5d8Q0j1nCF1TzPdTKAgb2+xO832XONPTP2URHJhzqzGeh8pnxdy7F34Y67A9zTFKr2a7c/RESJyoiq1kbN83wsx9P8OVmBGQwDe4qcZ/mmHhl70WnTbI38xEEkMl0Ze9tRNnaKtojW2PONICp8vamLjH3VfsDpiP4+NRVKGWaYo470WGcfbik+AGTmy5dVJdrtDxFRoor2uDug8ed1vTP2ACswVWBgT5HzKsXXao29R5mNko0yMFsPyC8o4kMq19lHl1KKH0bGnl3xo0P8fj1L8TPz5XFqTjv/z+hJ+VAXZhmmyNjvXwNUa3ScRGAfavM8wCNjz8CeiCgoIzL2jQJ7ndfYAx4VmPw85w8De4qcCOxNZrmsMxK+MvbKC5aB6+sBwGTyWGfPICWqRMY+rOZ5PMMbFcrZeo/A3pLkDtK4zl4/kTTPA+RqpFa95Ov7ftNmn+wR7FNWgXzJwJ6IKLiYGHenc1d8wCOw5xp7fxjYU+QcGo26A/wE9jEw6k5Q1tkzsI+qSJrniSx/PQN7XfmaYw+4y/ErGNjrJpLmeYI4Tlq9tjnEHPtIMvZcY09EFJQRCbDUhmvso1CKz6WVQTGwp8gpM+wjXF8PBM7Y6/lioZZYZ8/APrpqI1hjzzVZ0eFrjj3g0RmfDfR0E2nGHgDSNW4Oyow9EVF0KO8BCTzuDuDSShUY2FPkHK5SfIvOgX1MZew58i6qIlljn8I3gqjwNcce8OiMz4y9brTI2IvXtmOHI98fALCLjH0EgX01A3sioqBiYtxdNLriM1ETDAN7ipxYY98kSvFFxp4j76IqosCepVtR4WuOPcCMfTRokrFvIV9qFti79ims5nkeGXtJ0mZ/iIgSVUyMu2MpfixgYE+RE6X4WoyiC5ixN7grPgBk5MmX1czYR1Wd6+8hnOZ5LMWPDl9z7AHOso8GLV4jMzQsxZckwKHBuDtHPVBzJPL9ISJKZIY2z4tiV/xkNs8LhoE9RU6U4mu5xr6uEnA65esxlbF3BfZcYx89kqRR87xq998Uaa/ex7g7gKX40aDFDGOxxv6YBoG9aJwHhNc8LynFXUHABnpERIFpsRwrVKm58mV9tRwHiIx9VLriM2PvDwN7ipyWzfOUUmvJ3WVbyUbFQvM8BvZRZzsGSK6APKzA3iPQtB3VZp+oMTXN83hiRR/i5GckjZNEIK1Fxl6caAAiGMEnyvEZ2BMRBWRIxt6jgrKukmvsYwQDe4qcWGOvRSm+NdX9QbBheQ8z9k2T6IhvsoRXapyUKt8X4JuBXhw2d+l1w+Z5WQUATPIJQC2ywdSYFhl7LZvn2T0y9uGssQc81tmXRr4/RESJzIjA3mJ1fyarrYhOV3yusQ+KgT1FTss59oD/ERoxEdi7mucdK3MvQYi26oPA1u+aTlMpzzJ8kyn0+5tMLN/Sm+ebbMOqCovVvWaa5fj60DJjX1/tbsYXLqVxXkp4/2cBzrInIlJLi/eAcIjP6zVl7tf9qIy7Y5LGHwb2FDmlK75Fm8drFNiLM5Ex0DwvvTlgMgOQtOseHaovbwHeORfYsdSY54820SQlnI74gnKWN07eDJxO4LMbgWXPGL0n6ogTJpYU35U7OWygpystMvapOe6Ts5FWVjgiGHUncJY9EZE6RvWiEp/XKz1OwOqasXd9DmRg7xcDe4qcQ8Ou+EDjERqx1DzPbPFYi2pQZ/zS9fLlgT+Nef5oE4F9OB3xhXg7y1u6HljzP+D7R43eE3WCra3jyDt9KePuIniNNJm0W2cvTjSE0zhPyOIaeyKioCTJuOlR4vO6eJ02J4W//ErV88XZZzkDMLCnyGk5xx7wX4qv51nAUBi5zt5hBypc5cyVe6L//EaIpCO+EG+l+BWuY1tf5b1eOVbV+emIL4jO+BVN5G822pSOyBFkyAHt1tkrgb0GGftqrrEnIvLLXgfAtTTTsIy963OpNSP85VdqcI19UAzsKXJazrEHPAJ7V6Y2ltbYAx6z7A0I7Kv2AZJDvl7RRNYr12pRiu8KOOPlzcBzLXpNmXH7oZaSsfdz8oUZe31pVdUkMvYRB/aiFD8l/MdQ1tizFJ8oJKUbgE9vAI7sNHpPKBrEiV3A+FJ8vRNwovrSUe86oUENMbCnyDmitcY+xgJ7IzL25bvc15tKkKRFxj7eSvE9j+2xOArsg2Xsm8rfbLRp1ThJZOy1KsW3RBLYe6yx55hEIvV+eRlYOwdYNdvoPaFoEK//5iTtEmxqic/rVa73dr0De8/PgfFSgRllDOwpcs5odcWPgeZ5AJDp6oxvxBp7r8C+iZQ1K83zIinFj7OGK/GWsfc3w15QMvZNpMpELVstsHVxZJkHh81dxRNpKb6SsdeqeV4Egb2YpOC0xcf/AaJYUe36bFK21dj9oOgwssG06H0kTton67wPZo+xx+KzIXlhYE+R03KOPRAHGXuNslrh8Ars9zeNTJYIxiNpnhd3pfhxmrFvOMNe8MzYN5UxjWr88iLwzgQ5wxYum0cZZqQZ+3SNM/aRBPYWq3t/2ECPSD1RTVi2zdj9oOgwcrmqUoovAns/nwG0xHX2ATGwp8gpc+yTtHk8JbAvly/rj8qX1lhpnufK2FcbkbHf7b7utBmzHCDatMjYx10pfpxl7JVSfD/HKKu1PCbSUcdmaJ4ObZYvD28J/zFEEA1TZIE0AGRotcbeVYEQ6f4o6+z5N0Okmqi4KdvOE6lNgU2DZqXhEp/Xo9nkOt4+z0UZA3uKXJPL2Bu5xr5BM5ymUI6vSfO8OOqKL0nxl7GvC5KxT0oGctvJ1w+zPFQhMuORBNLK+vrUyLsRa5axF4F9hB80OfKOKHTi/299ddM4+d/UGblcteHnsmjsQzx9njMAA3uKnF4Ze5GpjbXAPlME9gaW4ot+Bk2hGZnSPE+DUvx4WJN1rMwjC4v4ytgHOlvfvLN8GUl2OtGIgL7mSPiPIf5WIl1fD3iMu9OqeV6E84w9G+gRUXC2Gu8SZZbjJz4jPyOn5np/HdVSfGbsfWFgT5HTs3meJMVe8zwlY38gumVuDru7RLuwv3zZFEbeadIVP47WZDVsMHcsgqAvWoLNsQeAFl3kSzZ0chMBdCRVGaKyKZL/H4JWGXuleV6kGXtRis+MPZEqDf/vlm03Zj8oeuxGBvY53l9HoxRfydgzsPeFgT1FzunqyGzReo19RYOOzzGSsReBvaM+uhngqv3ysgezFThukHxbUyjFF7/jiJrnxVHpVsPAPi4y9q43WH9z7AGghcjYM7BXHHVl7CMpxRf3FUF5JETGvrbcPcY0HErzvEgz9q7O+OzLQKROw2obZuwTn6EZ+4aBfRQScMoa+zj4PGcAQwP7H374AePHj0dhYSFMJhM+++wzr+9LkoTp06ejdevWSEtLw/Dhw7F582avbcrKynDppZciOzsbubm5mDx5MqqrvQ/277//jtNPPx2pqalo27YtHn30Ub1/tKbFoWPG3jPDGo0zgWpY09wBTHUU16+JMvycNkBOW/l6kyrFj2TcXRw1WxGBvckiX0ZSph0tosFlwIw9A3sv9jr3CZGasvCrf0Rgn6FBYJ/WDIDJvU/h0myNPTP2RCFplLFnYJ/wDO2K3yDhwoy94QwN7I8ePYp+/frh+eef9/n9Rx99FM888wxeeukl/PLLL8jIyMCoUaNQW+tef3rppZdiw4YNKC4uxldffYUffvgB1157rfL9yspKjBw5Eu3bt8eqVavw2GOPYcaMGXjllVd0//maDKdOa+wlp/tNypykXXM+LSgj76IY2Fe4OuLntnPPBW8KpfhaNM9LjqNxd+JkTctu8mVcNc8L8KaulOJvc1f5NGWeWXqnPfwPKeI1Usygj4TZ4gruEVk5vgjsLZF2xecae6KQiM8kJtfHewb2ic/QOfYNM/bRWGMfR5/nDKBRJBaeMWPGYMyYMT6/J0kSnnrqKdx7770455xzAABvv/028vPz8dlnn+Hiiy/Gxo0bMX/+fKxYsQKDBw8GADz77LMYO3YsHn/8cRQWFuJ///sf6uvr8cYbbyA5ORm9e/fGmjVr8N///tfrBABFQGTstSrFT0qVs/9OmztTEyvr64XMVsCR7fI6+2gRGfvcdnLWHmhctp1onE53VjOi5nmu+8ZD6ZY4WZPfGzi4MU5K8YN0xQfkKhNLsjzyrmIP0Kx9dPYtVjUMnI8dDm+5iVKKr0FgD8gnLWvKImugp/m4uxL5tcDM1YNEAYnXlVa9gNL1DOybAiPH3SWlut7XXX1VotoVPw6aIRvA0MA+kO3bt6OkpATDhw9XbsvJycFJJ52E5cuX4+KLL8by5cuRm5urBPUAMHz4cJjNZvzyyy8499xzsXz5cgwZMgTJye61fqNGjcIjjzyCI0eOoFmzZo2eu66uDnV1dcrXlZXyH4/NZoPNZtPjx9WM2L9o7qfZboMFgAMWODV63qTUHJiOHYK9fA+SAEhJqbDH0O/ektYCZgCOyhLNfuaGGh5LS9kO+Tmz28CZng8rAKlqP+x1tXKmLRHVVUHUadgsqUC4v2tzivz7qq+Cvb4+8rFgYVD7f9NSsUc+zq16wQJAqjli2D6rlVRXBRMAuyUNUoCfL6lZB5gO/QX7gb8gZRZGbwd1EOlrramy1OsN2F51AFJWm5Afx1J9SP57ScnV5LXIktYcZgD2ytKAxzIQs61Gfk8wWyPbp5RmSIIJJskBW8V++YSqTox47yT9NNXjaa4qlf/vFQ6EpXQ9UFsOW+UBdyVOnGqqx1MNc121fMwtqbp9Hg0kKSUbJteJYLslNej7RqTH0pyUDgsAZ20lHE3k7yGU31XMBvYlJXLpXX5+vtft+fn5yvdKSkrQqpX3G31SUhKaN2/utU3Hjh0bPYb4nq/A/qGHHsLMmTMb3b5w4UKkp8dY5tiP4uLiqD1X391b0BHA5m07sKlmniaPeZbDgkwAm1Z+j94AjtqAb+dp89ha6Hu4Rv6Z1/yETaUFuj6XOJanbF2NPABrdpRhb8UqjIcJJqcd3335Pmqt8f2m7U9q/WGMAuAwJWHewu/CfhyLoxZnAzBJTiz46jM4Ii0RjkCw/5tnlWxGJoBfdh7DKQBMTjsWfvUx7JbYfe0ZXV2GFAA//LIaVb/7X55yoi0TrQH8sfQLbP/zWNT2T0/hvtYeV/YTBnt8veKHhTiQHXrJ+Yk7NqI1gHVb92JnReSvkSdU2lEI4I8VP2D7jvCa3w3YuRXtAPy5ZTu2VEW2T6OSspFqr8CP8z9GZbr+VR7RfO8k/TW14zlg51q0A7CppAYdrc2QZjuCn756F+UZnY3eNU00teOpxvF7/kBnAFt37cNGAz4ni8/rALDy940o3aluH8I9lm0Pb8dAAAf3bMPPMRQX6OnYMfWfl2I2sDfSv/71L9x2223K15WVlWjbti1GjhyJ7OwIyoGjwGazobi4GCNGjIDVGp016Zavi4FDQNduPdD59LHaPGbJk8D+UvQ4LhfYB2TktsTYsdo8thbM368FflyMroXN0HmMPvvV8FgmPT8dANDvjPHo2/ZkYGtroGofzhrcE9JxA3XZB8Md/BPYAJjTciI7/pIE6ffrYIKEUcNOATLzg99HY6r+b0oSktbJS4ROGHUxpFeeh8leg5GnDQaadYjezoYoaZ18Nvn04WPcjR19MH/7C/Dzb+jdOhU9R8bO/+dwRPpaa/51N7DT/fUJvTtB6hP678Qy+1mgEjj+pGHo3T3y36l53rfA6pXo3TEfPYeE93iWTz8FyoAevfuh24mR7VPSvseA0nU4vX8XSF1GRPRYgRjx3kn6aarH0/L+20AZ0G3gqTCv2wPsWo5Te7aGdHycvN5W7JarCxos62qqx1MNy9fFwEGgc/fe6KjRZ/CQnt/1eR0ABhcNgdTh9IDbR3osTX86gF2vIi8nPabiAj2JynE1YjawLyiQs6ClpaVo3bq1cntpaSn69++vbHPggPcaZ7vdjrKyMuX+BQUFKC31HpUjvhbbNJSSkoKUlMYZPavVGjcvKNHdVycAwGJNgUWr50zLlR/zqHysTNb02PrdZ8t/k5aaQ9r9zH5YrVZYLWZltF1Si46A1Sqvs6/ah6RjJfLXicgun6U0pWRFfvxTsoC6SlgdtYb+vgL+3zxWpowKszZvB6Q3Byr3wmqrjN1j7LC79zk9N/B+5skNAS1Htuv+/yZawn6trSv3+jKpPsxjXCOvsU/KaqXN30iWXAVnqT0S/jFyNVS1JKdFfpyzC4HSdUg6djAq/wfi6X2egmtyx9PVkyUpu0CeRLJrOZIqd8fu+4enPauAN0YBXYYDf3/f5yZN7niq4ZTXt1tSM415X3V9XgeApPQc1X9rYR/LNLlhn7m+GuYm8rcQyu8pZjvRdOzYEQUFBfj222+V2yorK/HLL7+gqKgIAFBUVITy8nKsWrVK2ea7776D0+nESSedpGzzww8/eK1PKC4uRvfu3X2W4VMYnK55x1p2rRedNkU35FiZYS8oXfEjaDAViqoS1wz7JHdDqabQGb9Og8Z5gtIZP4ZHpIhmiBl5cuOxtOby18dieOSd5+8zWEfc5mLk3Rb99ideNGqeF2aTRK2b56Vr8Nqm1bg7gJ3xiUKhTMloCTTvJF+PlwZ6Pz8vnxTctoSTU0Jh5Lg7wLvpa1S64otmyDH8Wc5Ahgb21dXVWLNmDdasWQNAbpi3Zs0a7Nq1CyaTCdOmTcP999+PL774AuvWrcMVV1yBwsJCTJgwAQDQs2dPjB49Gtdccw1+/fVXLFu2DDfddBMuvvhiFBbKQc/f//53JCcnY/LkydiwYQM++OADPP30016l9hQhrefYAx6BfYx2xc9w9XaojlJXfM8Z9qJRXlPojF+nwag7QemkGsOd8cVJGnHSJt118jGWO+OLGfaWZCApyJpsMfKufCdgr9d3v2Kd6Dovmlp5jr9Ty2EDaivk61p2xQ93fwSHRl3xAXdgX83AnigoMe4uoyXQzNVfKh4C+6pS4I8v5Ov2mvjY51hh5Lg7wHvkXVS64rtOHjCw98nQUvyVK1di2LBhytci2L7yyisxe/Zs3HXXXTh69CiuvfZalJeX47TTTsP8+fORmurOAvzvf//DTTfdhLPOOgtmsxkTJ07EM888o3w/JycHCxcuxNSpUzFo0CC0bNkS06dP56g7LSlz7DXszC7OAIosTXKsBfZ58mW0Mvaeo+6E7OPkyyYR2GdF/ljx8GYgjqU4tkrGPoYD+zoVo+6ErALAmgHYjsrBfcuu+u5bLDvqCpxbdgd2/xzeyZsaUclh8iqHjEi6+JuLILDXatwdwIw9kVr1R+WgGJAD+3jK2P/2tvuzJCCP6mvK7w+hEIG9EePuAO/APjlD/+cTnwc5x94nQwP7oUOHQpIkv983mUyYNWsWZs2a5Xeb5s2bY86cOQGfp2/fvli6dGnY+0lBOHQsxReZn1jL2Ge6Avu6CnmGqFXnF1SfgX0TKsUPZ753Q0opfgy/GVTuky9FYC+CrJjO2IcQ2JtMQItOQMk6uRy/KX9wExn7ll3lwD6cQFopw2+u3YnVmCvFF7Ps90f+WESJTGTrk1Ll1+PmHd2311Zq8z6qB4cdWPWmfD2jFXD0AFCyHuh9rrH7FS+MztineAb2USjFF8/hqJffa7Q4gZxAYnaNPcURscZe01L8XO+vY22NfWquvN4dcH9A11O5q312jkdgr5Ti79P/+Y2irLHXImMvSvHVdxeNusoGpfhxkbEXx0jlG7ooxz+8VZ/9iRciKG/ZzfV1GH0UlPW0GpXhA96l+E5neI8hAntLeOPyvDBjT6SOqAJKbymfRE3NcZ+oO7LduP0K5q9v5Pe+9BbAKTfLt5VuMHaf4okS2Bu1xt4V2JuTgi/H04Ln58FYXlppEAb2FDlRPqVHxl6ItcDeZHKX40djnX2gjH3V/sRtNFPbxNbYNyzFT7SMPeAR2DfhBnpOh/tkjQjswznGWjfO83wsyQHUlof3GK4pCZpk7DPFGvvSxH2dI9KC5/p6IR7K8Ve8Jl8OvAI4bpB8vXS9cfsTb5TmeQavsY9GGT4gV6eJnzWWEzUGYWBPkROl+JqusW8Y2MdYKT4Q3XX2FbvlS8/APjNfPkMqORI3m6VHxj4eSvFzGqyxr4nlrviu5nlqM/bsjO86nq5laOJER0Sl+BoG9kkp7hNp4a6zd9S7HytSGXmAyQxIzuj1NCGKR6J6MJ4C+0Ob5S74MAGDrwbye8u3V+yO7fe9WCJOpOq9JNQfJbCPQhm+EA+f5wzCwJ4i59SxK74Q04H9QX2fR3IC5T4Ce7PFvf40Ucvx61wdv7UI7JNjvHmeJPnoih9Hpfhqz9aLQDZWP2hGgwhQU3OBTNeEDXstUH8stMcRfxfi70Qr4kRBuIG0krHXILC3JLmnkHCdPZF/SsY+z32bEtjHaCn+itfly26j5c83abnuJYelfxi2W3HF6DX24vOKeC+LBuXzHAP7hhjYU+T0nGMvxGJgL17Ejupcil9VKp888ZxhL4gX1Mo9+u6DUZTmeTmBt1MjJcbfCGqOuDsaZzVYYx8XpfgqT760cGXsK/e6s/1NjWdmLSXL3a8j1ON8zGNNrZaUdfbhBvYaZuwBrrMnUuNooIx9DAb29UeBNa7m1ydOcd8usvYsx1fH6Dn2BX2Aia8DE16M3nMqSytjNFFjIAb2FDk95tg3XFMda2vsAfebp87loaYK1/r67OPk7JUnZeRdombstSzFd/1N1cfoG4E4hukt3SV1yozzGC5JFCdK1Jbipzd3/1xNNWt/rEGTK5EhD7X0/ZgOzfPEfgGRZ+wtWgX27IwfU1bNBt6/lKXSsUZppukZ2MfwLPt1c+WqvOadgE5num8vOF6+LFlnzH7FE4fNnVwzatydyQT0OR9o1TN6z6mU4sfo5zkDMbCnyDmjscY+FgN7V8Ze7+Z5FT4a5wliLXaijrxTmuc1gVL8hh3xAXeJdX2VOwsaa0TWPZT1dU29M37DzFq40w/0WGPv+XjhrLF3Ot3Ls7T6oJmVL19Wl2rzeBSZH54A/vwK+OVlo/eEPAUqxa/aF/pSHz1JEvCrq2ne4MmA2SMcUTL27IwflCjDB2KzslUvzNj7xcCeIufQoSt+cgZg8jhREIsvWFFaY2/ytb5eUDL2CV6Kr0lX/BgvxW/YER9wneAyyddjNTtWH+Iae4AN9Dznz3tehl2Kr3FgnxFBYO+oc1/XrBSfGfuYYa93v9/8+ipgqzV2f8jNV/O8tGbuRMmRHVHfJb92/wqUrpNP/vX/u/f38vvIlwc2chJGMEpgb2pa89y5xt4vBvYUOT3m2IsZrEJyDAb2mVEK7H11xBcSvhRfw4x9rHdRbdgRH5CrYNJy5euxus5eKcUP4RgxYy9fipLZcJskKs3zYqgU3+4R6HGNfeKp2C03dAXkQHLdh8buD7n5WmNvMsVmZ/wVr8qXfc5v3PyzeUc5mWOvia19jkWeo+5MJmP3JZqYsfeLgT1FTmmelxR4u1B5BvZNOGPvc9SdkMil+A67+01Li+Z5yTH+RtCwI74Qbpl2tIQ6xx5wN9Ara6KBvciEa1WKn6F1xj6C5nliyYjJ7G4KGClm7GNHwyZsy1+Qy6rJWJLke409EHuBfV01sOEz+foJ1zT+vtniXq/NdfaBKR3xDVpfbxRRgRmriRoDMbCnyOnRPA9oENjH8Br7o4fkdaU6MQVaYy8y9tUlciCcSDybomgxHzUlXtbYH+d9e7hl2tES6hx7wB3YN9lSfD8Z+1COcf0x94mvWMzYW1K0yyAxYx87jrgC+w6ny6/LBzcCW78zdp9Ifl8Ty2Ay/AT2R2KkM37J73IfjqxCoLC/723yXQ302Bk/MLvBo+6MomTsK43djxjEwJ4ip8yxb2oZe9ebp+TQb/2z5AQqXOsZfQX2Ga3k37vklIP7RCIa5yWlAknJkT+eZyl+LGaYRCl+w8A+1jP2dWFk7MUa+2OHY7d3gJ6ONsi0h9OsTmxrSdbmxJenSNbY213BhZbrPTNdgf3Rg4l3AjPeiHXarfsBAy6Xry9/3rDdIRdxstCa0bjfSaxl7PetkS8LB/jfRgns2UAvICVjH4PJLz0pFZjM2DfEwJ4iJz5oadk8DwBSPRqmxeKLlsXqHtul0yz7VHsFTI56uZFgVmHjDcxm9+2JVo6vZeM8wB38OO3e64BjgST57ooPxEHGXjTPCyG4TMl0l1cfjpEPm9HUcExdOCdvPBvnab220jNjH+pJMJE11HL0UkZL+TVQcuq/9IkCE6X4zToAJ10nL7nY+i1Q+oehu9XkKevrfVTvxFxgv1q+DBTYKyPvmLEPSAT2Ro26MwrX2PvFwJ4ipzTPa2IZe0D3dfZpda43a18z7IWcBO2Mr+UMe8A78Iy1s7y15e6y6nhbYx/qHHuhqXbG97UWNpKMvdZl+IC7GslRF/oaRiVjr0GVjWC2AJmukXdcZ28skbFv1lFuctbjbPnrn18wbJcIvkfdCc1cs+wr9rj/fxpp/xr50l8ZPuAeeVe5J3bf+2KBramW4rs+b4RTVZbgGNhT5Jw6jLsDgNRc9/VYfdES6+wr9Amq0+tdAYCvMnwhUTvja9kRH5CrG0RwXx9jZ3nFsUtv0bg6Jd1VFRKrJevhNM8Dmm4Dvboq92tmRgRr7PXqiA/Ir7ciAxTqOnu7Dhl7gOvsY4EkuQP75q5gsWiqfPn7h0C1PpVrpIK/xnkAkNlKLtGXnED5rujuV0N1VcChzfL11v39b5eaA+S4PvccYDWIX021FL9lN/ly32/AsqeN3ZcYw8CeIufQeY29yaL9SQOttD1Bvlw3V5eHVxfYJ3gpfqpGpfiAx+xTDQJ7hx1Y8giwZ1Xkj+WvIz7gztjHYmDv8FjWEOoJmKbaQM9zLaz4MKZUZYRwjBuW82vJZHIHCKFmyzyb52lJBPaJ1ksknlQfAGxH5fL7nLbybW1PAo4bJFd3rHjd2P1rygJl7LUcebfhM+CZAcBv74R3//2/A5CA7DbukcH+sBw/OM9xd01Jq57A8Jny9eLp4f89JiAG9hQZpwOAaw2mXl3xY3k+56BJAExyV2AdZnKrCuxz2siXlQkW2NdWyJdarbEHPDrja1CKv+lrYMmDwFfTIn8sfx3xgfBnnEeDZ5l2w4ZNwSiz7JtYYH/URwm9OMb1Ve5xccHoWYoPeDTQCzdjr1Ngz4y9cUS2PruNe6mFyeTO2q94zZ1BpOgKNvpSVFhEEtj/9g7w0VXyY8z/Z3gVGsr6+v7BtxXl+OyM719THXcHAKdNA065Rb7+5S3Axq8M3Z1YwcCeIiPW1wP6zbGP5RKjZh2ALsPl6yvf0Pzh0+tdZ+Fz2/rfSCnFT7DAXuvmeYC2DVcO/SVflq6P/ESB0hE/UMY+hgN7szX0QE4J7LfF5pQCvRzz0eQqNVfOggLqj7PegX24I+/0aJ4HcJZ9LBDj0pq197695zlyBv/YIbkkn6IvUMYeiDxj//OLwBc3yeX81gz5tf/7R0N/HDXr6wWOvAvO3kRL8YURs4ABl8l/lx9dDWz/IfTHqE6shqwM7Ckyogwf0C9jnxzjJUYnTJEv1/xP82wFS/Gh3Rp7wGONvQYZe9EdWnLK67wiEa8Z+3Ab5wHySTGTWc5SN6W1ub7WwprN7p4iao+z7hl7UYofbsZew+Z5ADP2sUC85onsr2BJkjvkA8DSJ4Cti5vWybpYoHTFDxbYhzjLXpLkAH7+P+Wvi24CLnlPvr7qzdArFdV0xBcK+siXBzZ6J5HIrak2zxNMJuDsp+Umno464L2/u//G/HHYgZ0/AcX/AV4oAv7bA6gpj8ruRoPGKVZqcpyegb3Gf06ik2tOgGx1LOg6Qm7yUrFLXn/W/xJtHldyIq3e9eFdTSl+dal8oiVW+xGESuvmeZ6PpUXG3jPzsWcF0HFI+I8VKLAXIxVryuQPWbG0LKX+qHyZHMYxSkqR/2+X75TL8bPytd23WKWUzDZocpXeQj7Garv8Ks3zmmu3b177E2bGXrfmea6MvSgHp+jz7Ijf0MAr5CZW5TuBdybI2daim4DjJ2p/kocaC9Q8DwgvYy9JQPG/gZ+elb8edg8w5E75PajLCGBLMfDdfcAFs9U9Xm2le+lVaxWBfbMOcsBqOxb6CYmmoqk2z/NkSQImvg7873xgx1Lg3YnyMtmULDmZk5Il/6urAjYvBLZ8K08iEkxm+WRA52FG/QSaYsaeIuPwLMXXOKBs1QOYvAg4/01tH1drZgsw6Er5+koNmwcdPQiLZINkMvsO+IT0loAlGYCkX5nqkoeB50+ObrZMj+Z5ugX2KyN7LDWl+E577M1sVWbYh7i+XhDl+E2pM76/pnehdsZXMnR+PshHSlljH+I4IRHYWzQO5o4bJH8AO/SXblNIKAilFL9D4++l5gDXfAeceJ1cql26HvjseuDpvsDS/7p7ppA+lFJ8f4G962RM+U7vz23+OOxy/xgR1I9+GDjjLveJ5eEzAJiADZ8Ce1U2kN2/Vr7Maeu/F4AnswVo1QsAYDrAcnyfRPO8pCYc2ANyj4GL5wCt+8nvWUufABbNAObdAXx6HfD+3+XL9R/LQX1aM6DPhfIJgTu3JkxQDzCwp0iJ8iiTRZ9MYtsTgndOjQUDr5CXIuxZ4X7zipCpYrd8Jasw8EkTs9mdzdKjHN/plNfXHdwIrPtI+8f3J5ZL8euq5QoJYfev4ZeeSpL7uInqC0/JHqPHYm2dfSSl+EDT7Izvq3ke4NEZP0ZK8cPO2Lu64mudsU9vDrRxTSHZvFDbxyZ1Go66ayi3HTD2UeC2DcBZ/wEyC+STzd/OBF4ZmngjWWOFJHn07vAT2GcVypMqnHZAfLbwp6YcmHMhsGq2fDLtb88BJ9/gvU3B8UC/i+Xrxf9R9/4Xyvp6wdVAz1S6Qf19mhKb6/W2KWfshdRs4PLP5NeeE68D+v0d6Dke6DRMfu9ocwJw2m3A1QvkYH7iq0Cf8/WrejMIA3uKjF4z7ONNZiug19/k61qN/HHNm5UCNc4T9OyMf3iLu2xp8wLtH9+fWO6KLz7gpuTImcljh8IvEa6tkEdIAe4TNA2FGvRFS7gz7AWlgV4TzNj7KsUH1GXIJSkKgX2YXfEdOq2xB4CuI+XLzcXaPzYFVn/UfTLTV8beU1oz4PTbgGnrgAkvyRnasm3A7LOBSjY/1FxtuTvJ4q8U32x2v95+dJVr7JwPhzYDr50FbP1WLoO/4C1g4OW+tx12j3yyYMdSYMui4Psp1j4Hml/fkGudvekAA3ufmuq4O3/Sm8uvPWMfBc59EbjoXeCKz4Api+R/w/8DtDtZrgZJUAzsKTJ6zbCPR4Mny5fr5mpSdqhk7NX0GAi1M/6BjcBjXYGfXwq+7Z5f3dd3LpfXyUWDrl3xI/wZRBl+y65AQV/5erjl+CKLldbMf6PIUMu0o6U+wox9y67y5V/z5Vm0Ys1+IvO3FjZd9FJQMcu+tgKQHPL1NJ2yDRkxtsYecAf225a4n4eiQ5y4TM119/0IJilZ7jlz1Ty5D03ZVuCts9kAUWuiCig5K/DYsxGz5JPR+1bLFRTF04H6Y+7vb1kEvHqWfDI/u42c2RQJC19y2wInXiNfL/6Pa/xxAPvWyJdqGucJrs74zNj70ZTH3ZFPDOwpMuIsMQN7oP0pQF5P+Qzq2g8ifjjTnhUAAKl5l+Abh9oZf91HwNEDwIpXg2+7+xf3dacN2P69uueIRG2Fu1mOlmVSoslbpKX4IrBv3sldHuw6XiFTGuf5KMMXxAfpYyqCvmiqizBj33Go3FzLaZcbbz1/EvDnPK32Ljb5a54XSlWGeIzkTP0+0IkTD6FWieg1xx6Qs3eZBfJr7I4ftX988i9YGX4gue2ASV/KJ6kPb5Ez91Wlwe9H6gRbXy90HQ7c9CvQa4J8YnDZ08ALJ8vNxJY/D/zvAqCuAmh7EnDtYqB13+DPffrt8smCAxsCjzqsrXD3UgkpsHetsa/aB6tdg2k2icbexLviUyMM7CkyIrBv6qX4gNxj4ARX1n7l65GN+6mthGnbYgCAs9vo4NuHWoovxrMd3hK8NHK3K2AVnZCjsb512TPyB4yW3UP7EBCMVqX4XoH9YPm6Z2VDKJTA3kfjPCHWM/bhBvZmM3D+G8Al77smS+wG3r9EHllTHmQdaLzyV0IfSim+3mX4gLu5VX1VaNlxpXmeDoG9ySRPIQFYjh9tZQEa56nRrANw5ZfyCczDm+XMfVMac6knZXmPin5EWQXAhW/Jr7nZbeRmeu+eByz4P3l064DL5OOU2Urdc6c3B06/Vb6++AH3mu+GRO+h3HahnaxPzVGmAmXXJOh7QiTYFZ8aYGBPkVFK8RnYAwD6XiR3BD74J7BzWfiP89d8mBx1qEppLVcBBCOCQjWBvSR5z/kMlPmqrZB/FgAY6ppju7lY3xnFVaXAzy/I18+aru1aKK264iuBfUeg7Yny9ZJ17jfZUATqiC/E6hr7SJvnCd3HAFN/Bk67Va7+2fQ18PyJwLYoVIdEk63WfTIkkq740QjsU3PdlVihlOM7dCzFB4Buo+RLNtCLrkCj7tRq3lHO3GcfJ083eGs8UH1Qk91r0tRm7D2J19yTbgBgkpvkjXpIbpQXarXNSdfLzfkqdgMr3/C9TTjr6wVXOX5Oza7Q75voGNhTAwzsKTIsxfeWmg30vUC+/svLcmCsZrRMQxs+AwDsyz1B3bQBZY29iq7DR7Z7r+Pd8YP/bfesBCABue2B3ufK5V5V++UgVi8/PCaX2h43GOgxTtvH1qorvsheNe8kl5dm5sv/F8KZiKB0xA800lAEfTFWiq+Mu4swsAfkkXnDZwDX/wi0OVH+GxCjlhKFyKyZrXImylOsZexNpvAa6Nl1bJ4HAB3PkH9/ZVubVtNFowUadReK5p3kjHBWa/mk8eIHIt61Ji/c0ZcpWcCYh4GpvwLXLwOKbgxvupE1DRh6t3x92VO+T3CHs75ecDXQK6j4Td+kQjziuDtqgIE9RUZk7C0M7BWiid7GL4CH2wH3tQDuywMebg/8txfwzd2B719bqXSY3dfsRHXPKUrxqw8A9vrA2+51leGLKotAGXuxbrztSfJZ/E5D5a/1ypaVbQdWvSlfHz5D+xGKWmTsbbXuyojmneR9FOvsd4dRjq+U4gcI7NNitRTf1exOi8BeaNUTGP2QfH3/msT6IOcZkDf82w5njb2egT0Q3sg7vcbdCanZQPsi+Tqz9tGjnMyMIGMvtOgMjH1cvr5reeSP19T5a8ipVl43ZS172Pr9XT7JXV0K/PZ24++LjH0oo+6Ux74EkiUZedUbYfrrm4h2M+Fw3B01wMCeIqNk7FmKr2jdF+hzoVzaJjjq5ZE0lXuBX14C9q7yf//NCwFHHaTmnVGZqqIjPiB/wLemA5DcDWr8EW+wx58n72PZNv9N90SgKsrNlfWtOn2gXvyg/DfV+Syg4+naP74WgX35TgCS3K1fBFaRNNBTU4qfnuCl+A3l95argI4e1GeEo1ECZdbEMVZT5ROtwD4jhCoCQZxY1KN5ntCV5fhR5XQo41cjztgL4jXz4KbIl0Y1dUopvoo19npJSpaXUgHAj096r7WvOeKu+AinFL95RzhPuhEAYFk0nRMxPHHcHTXAwJ4iwzn2vk18FZheBtxTCty1HZi2Xi536zVB/v6yp/3fd8OnAABnz3PUZ6xNJncTt2DdokXGvuMZ7jdZX/dxOt0j3MSHMDFuas8K7YPMknXyqEBAXluvBy1K8T3X14vjowT2IY68c9jcH5gDjTWM2Yx9hM3z/LGmuXtLePaDiHeBAnJlhJgknwQMRIy3ytA7Y+96/HAy9no0zxPE69COHyNvhEnBVe6V3+vN1sCVRaHIyne95knuMm0Kz7EwS/G1NuAy+e+jaj+w5l337UrjvPZhT7lxnjoNtUm5MJXvkDv4k4xr7KkBBvYUGQfX2PtlMsmjqNKby/Ne87oDZ9wlf++PL3yvD62rVsrwnT0DzI/1peMQ+TLQODqnw/0me9xAoMNp8vUdSxtve+gvuTO9NV1pXoOcNkCr3nL33K3fhbZ/wXx7HwAJ6H1eeOV6aojMsqM+/LP+nh3xhcL+gMkCVO0DKvaof6x9q+VxNWnNAzelamoZe8D9N5BIgb1SMusjILdY5bFRQPDjHO1S/FDW2DuikLFv2VUOEhz1wPYAPUJIG0rjvPbaNjM9bqB8GaiCjYILd4291pJS3Fn7pU+632MjWV8vJGdiw3EXydd/eDz4NJ+mwOlwNytlYE8uDOwpMiJjz8BenfzermyTBCx/rvH3/5ovZ7yad5ID6FB0PEO+3PGjnG335eAmwHZU7tzfspv7ZICvwF6Mbysc6N1DQY9y/J0/AZsXyMHxmfdq97gNiTn2QPiZPhHYewbiyRlAgevkRyjl+CIo6XCqPPrNH5HNjbnmeSJjnxV4u3CID4GJlM0LlllLF8c5RgL7jEjW2OsY2JtM7I4fTZGOuvOnkIG9JpTA3sBSfGHA5UBmAVC5B1gzR74tkvX1HvY0K4KzcJD8GebbWZHtZyKweyx3YGBPLgzsKTKcYx+6U/8hX67+X+M5vn98Jl/2mhB647jCAXJJdM0RoHS9723E/PrC/nLmpe1JcjB9ZEfjueHK+voTvG9XPlAXy2eMIyVJwKKZ8vWBl8uNlfRiSXJ3j60Pc12nr4w9EF45vjih0mFI4O1EKX5dpbthZSxQAvsM7R9bCexXJ04DPSUg9xfYq1zTHrWMfThr7KMQ2APucvzNCxPn7yNWKR3xNWic5+m4QfKlWB5GoXM63ScMw22epyVrKnDaNPn60v/K71f718hfR5KxBwCTGc6Rrsaqa+eEvvQt0XhOH2BXfHJhYE+RcTBjH7L2p8ofaBx18kg8oa5aDpYBoPeE0B/XYgXaubpF+ytPVc6cu95gU7PdZ9F3LvPeVmSe2zTozN/mRLlkuKZMmw9kf80Hdv8sd9E+I8jEAC1E2kAvaGCvMmNvrwN2/SJfD9YoMC0XgOtETyxl7fUsxc/vLa/prSlz9yGId0pmzU9ArrYzfrQz9mE1z9OpK77Q4TT5w2zlXuDAH/o+V1OnlOJ30PZxC/sDMMnZ3apSbR+7qag5Ii+NA/R/PVBr0CQgoxVQsQv4+UX330/rfhE/tHTcQLkDPyBPGPJXndgUiMZ5lpTAFX/UpPAvgSLDjH3oTCbg1Gny9RWvuoOjzQvlbFezjkBB3/AeW1ln7yewF4G4WNsIAB1cQeV2j3L8mnJ5xjDgDlgFSxLQ5Uz3PkfiWBnw9e3y9ZOuC9wZXisiCA2nFN9hc1c2+Avs960JPnIQkMtP7TVy+WRej8Dbmi3uueexss7eYZf3H9CnFD8pxT2CKVHW2QfN2KtokuiwuZvr6Z2hi2TcnZ7N8wC59FS83rEcX19ajrrzlJLlfu3bx6x9WES2PjVH7kwfC6xp7srE7+6TL5t19GgQGqHh/5GrE/euBNZ9qM1jxiM2ziMfGNhTZJixD0+PcUDzzvJoKzHzVZTh954Q/vx28UF350+NR2bZ690l+oU+AnvPdfZ7XSVuzToCmT7W7SnjphaEt5+AXD772Q1yxq1FF2DIneE/Vigi6YxfvguQHHKmMKvA+3vNO8kZV0ed3OE/GHEipcPp6o63mqAvmmxH3df1yNgD3uX4iSBQ8zzP2wNlyJWKDZOrkkNHGTHaPE8Q/T7+YmCvK70y9gAb6EUqFkbd+TL4KvnEoHg90LIhblYBMOQO+Xrxf5ruuEQlsOeoO3JjYE+REcER59iHxmwBTrlZvr78eTlDLj6cipF44SjoA6TmyuvHxbo2oXS9/Cab1sz7A1o71zr78p3ukufdrnLytg3K8IUuw+XL/WuBqpLw9vXnF+UyfEsKcP6b7hJ5vaVky5d1laHfV8lcdWocjJtMoZXji6qKYGX4gtoy7WgRFQ/mJMCiU6ZIBPYN/5bjVbDmeWqOsQj605pp26HcF5GxrznS+EShP9FaYw+4A/vdv8TWEpVEUnPEXSHCwD72HI2h9fWekjPcn3GAyNfXN3TyjXLioboE+P4RbR87XjBjTz4wsKfwOJ3AT88Cxa5542HOJm3S+l0in2Wv3IP/b+++w5us2geOf5N07z0ptOy9NygOFFDcW0RwvDjA/br3z1d9Ha8bcW9xgwNBRUCUDS2jrLJL994z4/n9cZq0pSstLem4P9eVq22SJqc9yZPnPuc+9+HH21Vas3/0ya1D0xuqt7A7cds7W+G8EbWDUlfv6pMr6372SVVrv09Mw7fyCq6e9a/anq9ZUrdXv3amPQvhLVx60BInk4pfcw/7+lgLDVp3FGiIsaz6Pk0VzrNqbzP2Nfewb2mGSVM6UwE9s0kN4EEjqfh27H5wqtbXgxo8sK6VT99l3+9Yt7g6FYG9fzQE9VNZNK29/aZQrIOZXqFtUySzZgG9jv4edwTbjH07C+wBxtxcPVjZ0LlESzm5woyqgH7j25Cxp3UfvyOwrrGXwF7UIIG9aL6CFPj8IvjjMTUD3O88OEe2Hmk2ZzcYd6v6PuFX9bUl1fBPZFtnf8IWdinWwnkjqcO2n33VVnnW2ZOGZuyhuir1gWam45cXwnc3qK0S+89UH/6n0smk4jcV2Ns7Y5+0Rb13vMPt3wXAeoLUXmYmbYXz2jDTIniAyugoL6iuzN1RleUBGqBreCDUnlT8UxnYG5zUsiGAnV/Z9zu2wL6Ni+dZWXfpWHYPxH9/ap6zK2nLNHxQ27oaXFVWgPX42hxmI1SWNH2/zsp6PGhvqfigBtFnL4WL3q4u7Nua+k6DAReogb1l93S9QnrW7CgJ7EUNEtiL5tm9BBZNUGnEzh5wwetw9WKZsW+pMTepPeWtBl508o9pDeyPb6o+yYbqGfvI+gL7GgX0svarNHVnT3XS1ZC+VYH94TW1n6cxmqY+gPOOgm8UXPRW2832NuRkquI3VBHfKmIkoFNLGhqr8nysmevrofo91l5S8a3bBbq00fp6UMWgQqtegx19nb01Db+xFHp7UvFLmkjnb23WCtTx3zX9Pjeb1Ek2tN3yjBNNuku978oL4Ieb4Pub2s/gV2fQVlvdWTm5VGdsNXeXlYoieGMkPBcBL/eDj8+Dn++Ada+hS1iOk6kLBPztecYe1Nr6EbPa7nN++gvqMyhpM2z/vG2eo72SVHxRDwnsuypNg4TfYNUzsP0LtR9ofYFOWR4cWw9b3oevZ8H3N6gTqIiRcMs/aluTUx2YdSbu/up/CODXvXXWoQX3V6P3prLqfV4rS6qr3Nc3Yx81Tq2VLjgOu6tmvSJHqhm7hoSPAO8IFeDtX2Zf27Z/rh5fZ4DLPmy9KrnNYUvFb4PA3s0HQqoquVsLENbHmk1h7/p6qDFj314C+6qT5rZIz62pnRbQ0x38g94Zv6rjoT2aKpxX87ZGZ+yr+v9UDab2OlNllpTlNZ2dY64R+J+qGXvPILjpD5jykDqu7P4e3p6oBhzFybOm4rfVjD1UfyY1d5399i/UZxaotdaJ61Ux2j+fxOn765m67wF0Oxd37plc20BfO5yxPxV8I+HMR9T3K59o3g4eHZ01FV/2sBc1SCnzrihxI/z5ZPU66pp8ukFwP9Dp1d7AhSm1b9fp4bR/w5QHZIu71nL6v9Vs3qBLW2eQRKdTM8F7lqiZ4ehJqsidZlEn6D7hdX/H1UudXCVvgS0fqOsaS8MHtW/qyNmqcE3sJzD4ssbvn30Qlj+gvj/rMVW0zxGsW7M1NxXfYlYFBqHhwB6g22jI3KOCIGsac02VJdUnsNHNCOyt66/by4x9W+5hX5MtsN/Rts/THNmHMHw/h0EWI9qiP9WJ5ci5jQ+ENVU4D2rUUchTg6/1HQ9OZSo+qOyCoVfB+tdgx2IYeGHD9605o38q1thbGZzhzIdVMb0l8yD3MHx+MYy7Dc55+tS2pbOxpuK39lZ3NdnW2TcjsDebYNPb6vtz/wPdJ6p+zzkMOYfQkrfhmn8Mlt0JOxfD+f+DsMGt3nSHs2fAsLMbewvs+Aoy4uGPx+GSRY5u0akhM/aiHjJj35Vk7IXFV8HH01VQ7+SuTthipoBX1dZdhclweBUcWlkd1PtGqe3NJt8LN6+Csx6VoL41eQTApe9Bv+mt95gn7mdvTXGsb7be9jtVQWZF1QxktyYCe4ARswGdep6cw43fd81zKoug5xkw6e6mH7uttDQVvzBFrYs3uIBPZMP3G3ql+rrjy3r/J7rkLaq+gG9U82bB2tsa+1ORig81KuPvbD8zb78/gs5ixKIzoCvNgV/vg3cmw6FVDf+OPQG5tY81c8OZAKc6sAcYXpWOf/APKM5s+H7WNZ96p7av2F+fbqPh1n9g9E3q582L4Ns5aqvP1mAxq2ybZffAokkqmOjsbGvsT0Fgn76regvdpuxfppY8uQeo/u42Sh17z3wYLv8Q060b2R1xDZqzJyRtgndPh98e6Xxbo7XX7e5OJYMTXPAaoFODOCfWF+qsZLs7UQ8J7LuCrARYehssmqi2F9MZVPr3ndtVQDnnZ/h3Ajx4DG78Q62bP/8VuPF3eOg43LMbZn0LU5+sf322aH+sgX3SFqgsrbG+vpFUf2sBPSt7qtj6RVVvORX7ScP3yzkMe39U35/7rJrtd5SWVsW3puH79Wg8aImerAbCLCZY9XSdm3WJVTsPNGd9PbS/NfanongeqKUlTm6q7kNLimu1tgN/wMHf0fRO/NXvGczn/lctKcnaB19cCl9eAdmH6v5eibXIVSMz9s5u1TU3GkrHd0RgH9xPBV+aWa21b8ipLpxXHxdPmPkKXP2VaseBFfDd3JYH9xaLqley/AF4ZSB8OhO2faS2D/3xVlVE1mJu1T+h3TBVQkGy+r4tU/EDeoKbrxoYytxr3+9sXKi+jrkJXOoJbAzOHA6dgenWjap2jWaGTQvhrTEdc5lGeWH9W07akwnUFXQbDaNvUN//el/rDea1ZzJjL+ohgX1nZa6E3T/Ax+fDwrFqFBNNfcDN36yC9xNTst39VXr0qLnqw7L7ePVhKzqegJ5qWYXFqLIz7JmxjxoH+qpMjIBe4Gln4DCq6sN0x5cNF9da/7paCtBnmuPTIVtaFb+p9fU1TX1KLVvZ+xMk1a6Qr7NuKdic9fXQddfYG5wgbIj63tHr7E2V8PvDAFjGzKPIvRuWMTerQdLx89VM9cE/4KNzqwN5K+sJeFP7TXs0kZlh7+O0Nuus/fYvG96WzPr+P1WF8xrT/zxV2NXgqnYd+f4G+2eDQd1320fw+jD4aBpseVet43bzheHXqTR/UNu+fnWNCryaoywP/nxaFXv75xXY86PKSmnu47Sl/OOApmYEvULa7nn0+uats0/aopaNGVxgzL8av69PBFz5Gcz6QWUdFKXBF5epvu0okrfBq4PUazFxQ/X1FnP1QG9XnrG3OvsJ9X/IToCNbzq6NW1PtrsT9ZDAvrPJP86A1O9wenMYfH8jJK5TAUa/8+Dm1eoDLqiPo1sp2ppOVx047v2purJxY8X5XDyrUyKbWl9fU59z1dr90hzY/2vd2wvTqrfKOu1e+x+3rbj6qK8VzTyBbk5gHzqwOhBa+YQtEHIyl6FL26Gub876eqguNFia2z72e665j31bay8F9Da/AzmHwDMEy2n3V1/v7g/Tn4PbN6sMg9IcNZNbk71rYWv2c31sxfNO8ZraQZeqQCpzT8N72pvbwYx9Tb3PrgruXVTq9vc3Nh3cW8yw82t4a7RKuS84rupyDL0KrvkG/n0ILl4IM/4Ll3+k/taDv8OH51QXmmvKgT/g7Qmw7hVV7G3V0/DdHJUu/t8oeLEnfDITfn8Udn4DmfsdkxWQV6NwXlsXyY1sRmC/8S31dciV4B1q3+P3mQq3b4ShV1dvj/b7o+0/2yLvGHx1tfq8KkyGT85Xy9rMpqpjQdUWmtaB367M3V9lBAKseV7tkHHoz/bfxy0lM/aiHhLYdyYb3sRp4Sj6ZvyCriRLrZuf8iDcHQ/XfKXWoImuw5qOv2Ox+uof03Ql7VFz1YnqsKvtfx6DU9Vae+pPx9/4lsog6T5RZYE4WotT8atOcu0J7AHOeET9L49vgIQV6leLD6DTzOpE2S+qec9v7TuLsfnZBq3NVFm940Jbp+JDjXX2O9r+uRpSlAFrX1TfT32y/r87qDdc+Ba2tZ5H1lbfVmpHKj40XRnflop/ik/kPQLUADE0vLbclorfDmbsrfpMhau+VMH9vp/hh5vrT2nWLITnbcHp/dNg6S0qoPIMVttp3X+wug5Kzb9t8GVwwwo1sJm1H94/q/H1veUF8NN8WHyFmjkO6AWn368GDbqNqc7CKM1RhU83vgVL58Hb4+C5SPhouloWcKqcivX1VrYCek0M3uUdg32/qO8nzG/eczi7wyXvwJlVg24b34Jvrmv+Z8GpUpYPX16p1tGHDYFh16jMt7UvwCfnVS+xc/dvvHBnVzL0Shh4sfqc3P29ys54dTD8+RRkHXB061qXSQJ7UZccCTqTbmPRoZHpPYiAc+/HaeBMKXLXlVlnhK2zaPbURxh+jbo018jZ8PdLcHStWk8f2EtdX5oL2z5W30++p/mP2xasM8zNLaLU3MDeNxLG365m5f58EmLOJKi4av1oc2frQaXDGlxVf5bmnpqAuj4Wi1pbnLxFrQcfcEHbP2etAnpmxxRmW/V/qmBgxEi1t7u5gVmgqDFqKdPWD9Ss4G0b1Np5e9fG21Lx65mxryytTr90RBXs4bNUrYz4b+Gc/6sbwFuL57WXGXurvufClZ+rIG7vj6oAoFeIGiCrKIKKIpxKshlbUlUY0M0PJt0F425peqlJ5Ej412r4+lqVUfLpTAjqp14H3caq7KegfnBkjUq7L0wBdDD+Njjr8brrwyuK1DE0Yzek7VKv+fR4MJbA8Y3w6YVw2QeN705wMiqK1ODB0b9Vthe07fp6K2tgn7VPBdoN7bax6R0V3PY6S2VGNZdOB1Puh8CeqvZQwnJVUPiab9Qxu70wVcK3s1VauXcEXPutWlbQe6o6riRtVjP5IGn4Nel0cMUn6r24Y7GqCVKUCuteVZeeZ8B5/1ODsB2dFM8T9ZDAvjOJGotxfiwbN+zhvP7nSVDf1flFqSDUmkLe2Pr6k36u7qqI3sE/IO5TddIPsOV9dUIaOqS6yJ6jWQPi5sx6a1qNVPxmzF5NvltlMWQfQLdzMcFF+9T11myK5tDpVNBXlKaCPv8ezX+Mk6Vp8NuDqn6H3hmu+lwVVmtrQX3VyUtlsUqFPxXPWVNKLOz4Qn0/40W1JrihwB7UWs99y9T2W//8T+0kUmJnkStrSm19qfjWYF/v7JiBnV5ngVcoFGeo9/qAmbVvtxasag9r7E/Ub7p6vX4zW2XRnEAHmPRu6CbOxzDpTnD3s/+xfSLUzP0vd8Our1Uwlp2g9lkHtfzHuvTHPxouelttQ1ofV2+IGK4u1pVTFrMK9v98StUL+PZ6OO8lGNvE+nJ7FGdByja1bv3YP6oei1bjta3Tq2CorXmHqd1GClPUYEZ9/5+yfNj+ufp+woKTe77Bl6mdSb66Rg2cvH+mesyhV9mf3t9WNE0F70f/VgPRs6qCeoAhl6tCcT/cDMlV9Vu6euG8E+l0asAtciRMe1YVjd6xGA6uhCN/qULSU+6HiXe1r+yi5rIG9u1tIFU4lAT2nYlOpyp2s8fRLRHtRfRp1QFpW+9oMGquOtnf/qVKdbQY1ZpkUAFuW6/RtJc1IDKVq/W29gyAFaWrtDedQQ1i2MvNF6Y8AL89hGHt8/iWVQV3LZmxBxX0FaU5rjL+3y/DlvfU95e8o9Ywnwp6A4QNVdtWpW4/tYG9xQIrHlTfD71azcQ2xc0XZryg1k2ve1UFEXbP2DeSil9znb4j3k8GJ5XquuFNdaJcJ7BvpzP2Vv1mwM0r4fBqFTC5etu+mgzu/B53lHOnXI7BuQWD4s7ucOm7ak/15K0qoyVpq0qXtgb1Y+epwprNLTipN0BwX1UjZ/m/IfZj9bUwVQ0iNfVaMJarYn1luep1lbGnqo3bID+x7v39eqgaLdGnq0HIEwvttpWIESqwT4mtP7CP+1QN7oUMVINMJytqrMq2WHyVyhRY+bgaPOlzLoyYpYq9OiLw++d/aiBRp4fLP64uHmrlH60Gkta+oI4vLf086QqcXFXR6IEXqWUcy+5VWzqv/g/sXgIXvGHfMb0lSrJVDQ1zperD8GFq8Kq1jt0yYy/qIYG9EJ1ZzOnqZEinVx8qbanPNFXXoThdzSoVWmeWY9Sat/aiZrG3iiL71irbtrqLan4mzOibYPM76KrWq2oBvdC19ES5qYrpbWnbR7DmP+r76S+omaNTKWJEdWDfnBoQLVVeoAKfhOUqCHL2VEGZvQZeBH2nq9mipfPUQBc0oyp+PYM39q7Tb0vDrlWB/cHf1YlrzbbYiue5OqZt9ogYUW8RUc1oxLQz4+Qf3ytYVeTvX1WPwGxSBQddvKqXKLWUwQlmvqpmb9c8q5b5FKXBhW+q41JhalXAvhWSY1XQXppbvRa3Xrqq7QxHQ4+JKqBvzuBla4ocpYocWteO12Q2wuZ31fcT5rdecOTfA/61CnZ9q3Z2Sd6qtkg8sEINoI2cA2c+0vYZkBaL+pw58BusfkZdd95LahlJfQzOcNZjcNq/1VIf0TT/aLjuB5We/9tDamvFD89RmS+n3996Oz8Upqpj5LaP67733AMgfKg6H+t1FvSY3PL6CFI8T9RDAnshOrPeU1Uac8SIU7MtmXWt/Zb3qwsvTbqrfRX2cXKpXqteWdy8wN7e9fUnPt9Zj8MPNwFg6TGZFq8Qt1ZMb6vAPjlWFWpy969x8VPB7a/3qfucfj+Mv7Vtnr8xtsr4O+y7v3X5RP5xFaSXF6iZ0/ICtaWY3kmdEDm7qxkPFw/1O2k7VFpy5j5UxekqU+5v3sylTqdOzI/+rVKLQQV3TZ2EN5aKb6uIX/s1m5BehK+7M2G+p+AEP3Sg6ovU7WrQcOKd1UGPqQME9qeawal1B1V1OpUF5B0Ov9yldhxJ3aFe10Wpjfyeofr9HNhbpXNHjlaZXO1lW1tbAb16KuPv+VHN5nuGwJArWvd5XTzVHuijb4CsBJWNsvNrNUi97hUoSIZL3lVLcFpLcaYK4tN2qaUAGbtrLw+bsADG3Nz040hQ3zw6nco66nU2/PGoev9seU9dAntD1HhV5Lf7BDUQ15wBpLxEWP+aWoJjrlqWFDECggeonUSy9qsB2yN/qcv611V9hIEXqV1Huk9o3mtMtrsT9WhHZ9tCiFbn7gcLtjZ5t1Yz8nqVrp24Xv3sFVa97Vt74uoFpRX2V0M+mcAeYNClWDYuRJ8ah9b7JGoNeDQS9J2sw6vh80sav8+ouXDmo63/3PawBvbpu9Qs6ImDRZqmTsoT18GxdWq/5+KTnIH1j4aocWpmZehVzf99v+5qts+69Z09Be8a6+N60vmT80q54M119Az25Le7W1C7oSWGVRWKW/V/sPpZlV7q30PWfJ5KI2eregffzVFp5KCC99CBqsJ+5GgI6a9eK+7+asu+1gxM20LEcECnBuPS41U9jeOb1CU9Xt1n7Ly2HTgK7gfnPK0GY3d/r3YxiP9WBf8zXz35TIHCVBXQxX5SvXTFyskNQgepTJ/T7ju55xGN8wxUy8mGXgkrn1SfKzmH1MVaT8UjEMKHqxn2sKpZdv8Y9T4yVUD2AcjYW7UF6G4VrFvrU3SfCKffpwYQrK8ZY7nKEkjfpZbpJPyqBtK3fqAu3uEqu3HABepzp6kJEZmxF/WQwF4I0Xr8uqssgUMr1c8T5rfP2TsXLxUk2VsZ37qfc0sDe70e89Xfsumn9xjTZ1rLHgOqZ3PrS9M+UX6SWhuesRuu+kKdnDSkskTN/oFaX6vTqayA8kJss9aDL4fzX3FcrYTA3qrfKourazcUp6v6B0Xpaoa9NLv27xhc1ayLmx+4+aiZSTdftbZas1RXmTeWqYu5EkIGqJOqqLGtk5o57jbY9Y0KTOxJobcnFb9GYL/9eD6VZgv704soKDXi63EKiqYOuwr2LFGF1swVaq/3guPVt7eXGeDOru+5cPOfalvF8GEqMG7rzKy25OarMsyyE+CdyXVv7zYGxtoxi90aDE5qyY/eSRWqi/1Y/W/P/U/LjoH5SWo217rmGlTQGD1Z9V3YUHWMa0/ZbV1Br7PUpTRXLcM4vhGOb1ZZI6U5aj3+4VXV93fxVsUV846BpZ5tM3udpZZH1FcjwtmtuqjfqLlgfk29d/csUcVWi9Jg8yJ1cQ9QNUH6nace88TdM6B6YEgCe1GDHEGEEK1r1FwV2Lv5qdTG9sjVR32trArsNU2lW6bGqVn88KEQ3L86xdg6Y38y+zm7+5HlM/jkAmNr0FeUptpc32NZzCqtcNUzakcCULN689aq4LY+a55Ts2S+UXDb+uoCgxazSvE1VZy6AloN0VfViUhcr1Io6+PkrgLy6MnQY5JK7XV0qqrBCS5aCF9fp1Ium1KzeN6JfWwduKgR2CekVw9OJWQUMTbmFOxv7+YLN/6m1gUXZ6i13HmJ6mtZHoy+se3bIJTQQerSWfSeqgJ7nR5CB1elRY9XKdKO2I5uyOVq8O/nO9S+967ecMZD9v9+zmHY8IYqKmuts9F9IpzxIMRMaT9FZbs6jwDoO01dQH3mpcdXbTe5Sy2ZyNyrzhlyqo65rr4qQyZkoHoPRo2DsMH2P6fBGfpMVZeZr6qsuT0/qvolZbmq5sOOL1UmR/Rp6rGDB6iskqC+NVLxpXieqCaBvRCidfU/H2a+pj7sHLXXelOseyRv+1gVZEqJqzvb6+SmKtlGjIDsQ+q6ls7YtxbPqhnkvT/BwrFqRmnoVeDbTV2fHg8/31ldfKr7BDVTlHtEnZhe8UndE8mUWNj0tvp+5qu1+0xvsK8GwakyYYFaJ+/qq2ZNvMNVOrJ3uEoFDx/ePrcvCh8G98Tbd19rVoa5UmVS1NzP2zZjXz3zvz+90PZ9QnrhqQnsrfR6NeDjE66CLyFO1tQnVXp0QM+GByJPtZHXq/fibw/BX8+rzKGJTWy3lxwLG16Hfb+o7CBQwdmUB1WBQtG+ObmqOhTdRldfZzap9PvidBVYt2aFeyfXqhn6Gep5jm9UtW32L1OD7odWVmdCAmqDzqpsOln6JGqQwF4I0bp0uvY7U29lnbHfv6z6Or1T1WCEjxqhryisrjANgM4xe8fX1G+GWt+8Z6k6wVj1f2pmPuY0COyjiplZTCrwPedpVdE5JRY+ng57f1Tr+GrufW02qoEAzaIKUvU5ifX/p0LNauOdlYun2gfeXKlmbWoF9nWL5+0/YcZeiA7NybVqrX07M/42lc215j8qY6goTQ36+kap3VK8wtT9Dv6hZuitdWYAep8Dp92rdh0QHZfBSc3Qhw5s++eJOU1dpj2nltMlblTF97ISVE0N6yCvi7cqwCdEFQnshRBdz5ibVIp5QE91chY5UqV9WtO2rVsPpe1QRcLSdqqtAx29ls3NBy5ZpPZI3/ezqtx87B9Vef3o3+o+Ay5U1di9q040o8bA1KfVyejvj6gZCGshuvWvq5MG9wCY/l/H/E2iNp1OpdoXpamTt5pbj52wxr6o3EhyXvV2SgfS7SwGKYRovtP/rVKx17+u0vJrMrioQWFr5pfeWQ2WTryj7QNB0XnpdCpzMGxI7etLslWg7x1ee/BXdHkS2Ashup6aa+nqo9dDUG91OdX7tdvDzQdGXKcueYmqanP6bpXC2v/8uvefMF9ViU/4Fb6dA7f8rarxrn1R3T79v47dG13U5h6gAvsf56vZeb2TWo9prfVQFdgfqJqh1+vAoqm0fE3T0Mm6XSFan06nBkkDe8Ox9VCQpNKkC1NVhk1ptppBHT1XFc10RE0A0TV4BoFnPQUmRZcngb0QQnRk/j3U3vKN0eng4oXwbrwqcPbTfFXkzFyhtuMZeuWpaauwT3A/tYVS5p66t+mdbTUVrGn442IC2XIsl8JyExmFFadmP3shuiKdTq25H3l99XVmExSlQlGGeu+2l9oAQoguRwJ7IYToCtz9VfG8D6dV1xZwbqW9mUXruvBNVRjRVK7qIFhM6mI2qjoQVWvsrRXxh3bzJau4gkOZxexPL5TAXohTyeCklszUXDYjhBAOIIG9EEJ0FZGjYNqzsOIB9fPZjzu+IKCoy9Wr8aUiVfanqcC+X5g3yXllHMos5kBGEWf0C2nrFgohhBCinZHAXgghupKx89T6+vJC9b3okDRNs2111z/Mh+S8Mn6NT6tVJV8IIYQQXYcE9kII0ZXodHDWY45uhThJ6YXlFJabMOh19Arx5HiuN1BdUE8IIYQQXYve0Q0QQgghRPNYZ+Z7Bnni6mSgX5gK7A9mFGO2aI5smhBCCCEcQAJ7IYQQooOxrq/vH64qcHcP8MDNWU+FyUJiTokjmyaEEEIIB5DAXgghhOhgEmzr69VMvUGvo0+IpOMLIYQQXZUE9kIIIUQHY03F7xfqbbvOmo4vBfSEEEKIrkcCeyGEEKIDMZotHM4qBqB/eI3APlRm7IUQQoiuSgJ7IYQQogM5klWC0azh7epEpJ+77XqZsRdCCCG6LgnshRCtLq+kEpPZ4uhmCNEpWfev7xvmjU6ns11vDeyPZZdQbjQ7pG1tpaDUyL+/28lPO1Ic3ZQur6TCREp+maObIYQQ4gQS2AshWtXulAImvbCas19Za0sXFkK0HuuMvLVwnlWItyt+Hs5YNDrVe0/TNO7/fiffxybzxE97qDB1rkGLjuaWz2M546U17EzKd3RThBBC1CCBvRCi1Wiaxv/9spfSSjOJOaVc+vYGNh/JcXSzhOhUEhoI7HU6HX2r1tkndKJ0/E83HOOPvRkAFJQZ+ftAtoNb1HUdzipm3aFsjGaN9/454ujmCCGEqEECeyE6CItFY0dSPq+uPMBjP8ZTUGp0dJPqWLE7nS3HcnFz1jO0my8FZUZmf7hF0meFaEXWoL1fmE+d26zBfkInKaAXn1zAc8v3AxAd6AEgxxMH+nF79f/+t93ppEpKvhBCtBtOjm6AEKJhZSYVLK89mMvaA5lkF1fabkvMKeXjuWNwMrSP8blyo5nnlu8D4JbTe3HbGb2455sdrNidzl1f7yApt5T5Z/autSZYCNE8BWVG2/rmfifM2AOdasa+qNzIgq/iqDRbOHdgKPPP7M1FC9fz574MiitMeLnKKcypZLFoLK0K7L1dnSiqMPHZxkQemtHfwS0TQggBMmMvRLtksWi8ueYwj24zcOc3u/ghLpns4kq8XJ2YNigUN2c9/xzM5qXfExzdVJuP1h8lOa+MMB83bpnSEzdnAwuvHcm803sC8PIfB3jwh10YpaieEC1m3couwtcNX3fnOrdbg/0DHTyw1zSNR5buJjGnlEg/d166fBhDu/kSE+RJudHCyr3pjm5il7MtMY/kvDK8XJ149tIhAHy15ThllVLzQAgh2gMJ7EWHE59cwNO/7OkUM1L1KSg1ctOnW3lj9WHMmo6eQR7cPDmGxTePI+7xc3h39mheunwYAO/+faRdpKVmFpWzcPUhAB6c0Q8PFzWTptfreOS8ATxz0SD0Ovh2WzJ3f7MDTdMc2dxmS0gv4q+ETPanF5JfWtnm7dc0jSNZxWQXV7Tp84iOZ78tDb/ubD1Uz9inFpRTWN7+luvY65utSfyyMxUnvY43rx2Br4czOp2OC4dFAPDTjlQHt7DrWRKXDMCMwWGcPyScqAB3CsqMtll8IYQQjiV5bKLDyCgs58XfEvih6uTit93pLL/zNPw9XRzcstazN7WQW7+I5XhuKa5Oei6PNvLUnMk4O9eembtgWAR70wpZ9NdhHvxhF72CvRgc6Wv38+SVVPL11iTO6h/SYIDQHP/7/QAllWaGRflx0bDIOrfPnhBNhJ87t34Ry6+70hgQ5s2Cs/q06LlS8ssw6HSE+bqdbLPtsie1gIsXrsdorg7mXZ30hPq4Eebrxs2TYzh3UFirPFdppYmfd6TyxeZEdqcU4uKk54ZJ0cw/szc+bnVnZ0XXsz9NbXXXP7zu+noAX3dnwn3dSCso50B6EaOjA05l81pFQnoRT/68B4B/T+vHyO7+ttsuHB7B66sO8s/BbHKKKwj0cnVUM7uUcqOZX+PTALhkZCQGvY65E2N4ZtlePl5/lGvGRskyKyGEcDCZsRdtIjW/jA/XHbW7wNvq/Rl8uuEY247lUlppqnVbudHMm6sOcubLf9mCeh83J9IKyrnvu51YLB1r9rchS7cnc+mi9RzPLaWbvzvf/GssY4Mb/tv+fW4/pvQNptxo4ZbPY8mxc3Z3T2oBF7y1jhd+28/l72xgV3J+k79TbjSTlFta7227Uwr4NjYJgCdmDkSvr//k7uwBofzfRYMBlZb/x57mp9Ku3p/BmS/9xZkv/2VXu0+WyWzhoR/iMZo1grxc8fdQwXWFycLx3FK2HM1l/uI4YhNzT+p5DmYU8dTPexj33CoeWhLP7pRC9DqoNFl4d+0RznjpLz7beEyWMYgGK+LX1K8DF9ArrTQxf3EcFSYLU/oGM++0nrVu7xXsxZBIX8wWjeW7JR3/VFm1L5OichMRvm6MjwkE4IrR3fB0MXAwU1XKF0I4ltFs4fONx9hwWN6PXZXM2ItWV1Jh4roPNnMku4RV+zL4/KZxGBoI9gBW7cvgpk+32X7W66pP3qKDPPl6y3FSC8oBGNHdjydmDsTVycDFb69n9f5MPlh3hHmn92rzv6utmMwW/m/ZXj7bmAjAlL7BvH71cDyddSTuaPj3DHodb1w9gosWruNYTikLFm/ns5vG4txIMb2fdqTw4A+7KDdaMOh1FJWrvlr8r/ENzvjvTy/kti/iOJpdwqAIHy4f1Y0Lh0UQ6OWqtrdbthdNg4uGRzCqh3+9j2F1zdju7Esr5LONidzzzQ6W3D7J7oyBNfszufVzVUgLM9z4yTZ+nD+Rbv4edv1+S3y0/ijxKQX4uDmx/K7JhHi7UW40k1VUQXphOe//fYQ/9mZw6xdx/HrHZEJ8mpdFYDRbuPfbnfyyszqtuEegB7PGdefyUVHsSMrjueX7OZRZzBM/7eGTDcd4eMYApg4IOanZsW+3JZFVVMHVY6JkxrMD0TStRkX8RgL7UG/+Sshqs+VKCelF3P/9ToZH+fHUBYMaHMxriSd/2sOhzGJCfVx55cph9T72RcMjiE8p4OcdKcwe36PVnls0bOl2Nah+0YhIW5/4uDlzxegoPtlwjI/WHeW0PsGObKIQXVpppYn5X8axJiELJ72Oj28YI+/JLkhm7EWre/yn3RzJLgFgw+EcXvvzQIP3Tcot5Z5vdgBqBirE2xWLBgczi1myPYVXVh4gtaCcCF833rhmBEtum8iI7v4MjPDhiZkDAXjxtwTijuc1+BzrDmbz1uqDfLstiXUHszmSVUy5sX0U+9E0jYeXxNuC+jvP6s1Hc8fg52Hf8gJfD2feu340ni4GNh7J4eEl8RzOKq6zBtxktvDMsr3c9fUOyo1qJuzvB85kVA9/CstNXPfhZvamFtZ5/J92pHDJwg0crerPPamFPP3LXsY9t4p/fbaNl/9IYMtRtb3dg9Ptq4z8+MyBTOgZSEmlmX99to28ksomf2fN/kxu+TyWSrOFaYNC6R/mTXZxBTd+spWCsrZZR5yYU8IrK9Vr97HzBxLirYJ2N2cDUQEejIkO4NWrhtM31Iusogpu+zKOSpP9M+qapvH4j7v5ZWcqBr2OaYNC+ezGsay57wzmnd6LAE8Xzuofym93ncYzFw8m0NOFI1kl/OuzbSz4anuLXsOapvHKHwk88P0uXvo9gckvrOH/ftlLetXAmWgbu1MKuOKdDby79vBJ1WdIyS+jqMKEs0FHzyCvBu9nm7Fvg8B+67FcrnhnA7uSC/hsY2Kjx/ea7Hm9LolL5rvYZPQ6eP3qEQ0OOs0cGoFOB1uP5ZGcV38mUUeWlFvarrJzcoor+CshC4BLR9ReajV3YjQ6HaxJyOJIVrEjmidEl5dTXME1729mTdX71GTRuP2LOPan1z2vE52bBPaiVf0Qm8ySuBT0OrhpcgwAb64+xJqEzDr3LTeaue3LWArLTQyP8uOnBZPY8uhUtjxyNh/OGc3dU/tw3pAwHpjej1X3ncGFwyJqzVLOGted84eGY7Jo3LF4e520/4zCcm7/MpbrPtzMy38c4IHvd3Hdh5s5639r6f/4b4z4vz+Y/2UcGYWOC2pe+j3BdiL79qyR3Htuv0azG+rTN9SbV64aDsD3scmc/b+1THh+Nfd9u5MlcckkpBdx3Yeb+XDdUQDmn9mLj+aOIdLPnU9uGMPwKD/yS43M+mCT7UOg0mThqZ/3cNfXOygzmjmtTxCr7pvC0xcOYmg3X0wWjZV7M1i45jAA807vRYSfu13tdTboeXvWSKIC3DmeW8r8xXGNnsSuSagO6mcMDuOta0fy0dwxhPq4ciCjmNu+iG1WQG0PVZE7nnKjhYm9ArlidLd67+fp6sS7s0fj7eZEbGIezyzba/dzvP3XYb7emoReB+9eN4p3Z4/m9L7BdWYonQx6Zo/vwV/3n8HtZ/TC2aDj111pzPpgM7l2DIrU/Jte/iOBN6qKHPYM8qTMaOaj9Uc5/cU1PLwknuM5nS9IOlmp+WV8vvEYy3alsjMpn9yS5hVPjDuexzXvb2LrsTyeX7Gfl35PaHFwbw3UewV74eLU8Me3bcu7jKJWLfS4cm8G132wmcJyEzFBngC8sfoQP+9suJBdhcnM3V9vZ/CTv/PqygMNtudwVjGP/bgbgLvO7sv4noENPmaYrxvjYlTtgF92prX0z2l39qYWMuejLZz24hpmvrHOtgOCoy3blYbJojE40oc+obUzRaKDPDmrXwgAn2w45oDWCdG1Hc8p5fJ3NrIzKR9/D2e+njeesTEBFFWYuPHjrQ49xxWnnqTii1ZzJKuYx3+qPjG7a2ofKk0WPt+k0q6X3TG5Vtr007/sYXdKIf4ezrw9aySuTgYAQnzcONvHjbMHhDb6fDqdjv9eOoTdKQUk5pTy7+938t7sUWgafLnlOC+u2E9Rhck2G1pcYSYtv4zU/DJKKs3klRr5NT6NdYeyefrCQVw0PKJViv9omsbhrBJcDHq6BzacJv7J+qO8/ZcKjJ+7ZAjnDQlv8XNOGxTGW9eO4Kstx9l6LI/0wnJ+iEu21SQA8HQx8L8rhzF9cPXzeLs589lNY5n9wWZ2Jhcw6/3NvH71CF798wCxiSoLYsGZvbnnnL4Y9Dp6BXsxZ2I0BzKK+CE2mZ92pOLv6cKtU3rWaVNj/D1d+OD6MVz69no2HM7hP8v28sQFg+oMaqxJyOSWz1RQP31QGG9cMwJng54IP3c+mjuGK9/ZyIbDOTyyNJ6XLh9aq/8qTGbWJmSx4XAOwd6u9A31pk+IF1EBHk0Onnwfm8z6Qzm4Oul5/tIhjb4uYoI8ee2q4dz06TY+35TI0G6+XDE6qtHH/2lHim2rwqcuHMTUgY2/1kH11QPT+3N632DmfbaN2MQ8Llu0gU9uGEOPQM9Gf1fTNF74LYF31qrX2+MzB3LjpGj+PpjNwtWH2HIsl6+2HOfbbUlcO7Y7j54/ADdnQ6OPuTulgB+3p+Dt5kykvzsRfm508/MgzNet0aCzJQrLjaw7mM3q/ZkcySpmZHd/zugXwpgYf9txoy0cyCji2vc3kV1cewDF08VApJ87/pqeoXllxITUX9hw05EcbvpkKyWVZqIDPTiWU8rbfx3GbNF4aEb/Zh9vmqqIb9U7xAu9DvJLjWQVVTR7iUh9vt2WxMNL4jFbNM7uH8Jb147ktT8P8O7fR7j/u510D/BgeJRfrd8pKDUy7/NtbD6qalC8vuogx3NL+e9lQ2r1W7nRzILF2ymtNDOhZyALzurdZHsuGh7JpiO5/LQjhdvO6LhLsUDN0L+y8gA/7kjBOu6RkFHEhW+t46kLBnHVGMcWpltSVfX+khH1D3DeODmGVfsz+T42mfvO7VfvNoxCiNYXn1zADZ9sIbu4kkg/dz67aSy9gr14b/YoLl20gSNZJdz06Va+mTcBT1cJ+boC6eUuzGzRKC434etx8h/CFabqE7PxPQNsJ2aPzRzAzuR8diUXMH/xdr69ZTyuTga+25bEV1uS0OngjWtG2D3beyJvN2cWXjuSS9/ewMq9GTy/Yj/bjuUSdzwfgGFRfjx/yRAGRlRXkNY0jcJyE4cyi3j6l73sSi7g7m92sGJ3Gv+5eAjB3s1bc1xWaWZXcj7bEvOIS8wj9nge+VXZAxcMi+CBaf2ICqgd4C/blcrTVbO7953Tl6vHdm/R31/TzKERzBwaQbnRzLZjeaw7lM36Q9nsTi2gT4gXC68dWWe2BdQ6yc9uHMesDzexO6WQ6z7cDIC3mxOvXjm83qCzb6g3D583gIfPG9Di9vYL8+bVq4Yz7/NYPt2YyOebEgn2diXUx41QHzcCPV1Ysj3FFtS/ee2IWvUDBkX48ta1I7np0618H5tMjwAPbj2jFxsO5/DLzlR+35NOUbmpzvO6OunpHeLFgHAfLh4eycRegbVmybOKKvjPr/sAuPecvk0GzaAKA949tQ+v/XmQR3/cTf8wH4Z0q79mwdZjedz/3S4Abp4cw/UTopvzb2N8z0CW3D6ROR9t5Wh2CZe8vYEP5oyuVTm8Jk3TeH7Fft77+wgAT10wkLmTVDbNlL7BTOkbzJajuSxcc4i1B7L4fFMiu1IKWDRrZL3vS03T+HTDMZ5dvq/WTgFWOp0a7LhhUgxXju7W7MDbYtEoKjeRVljGPwdUML/1WC6mGkUy447n88G6o3i4GJjYK4gz+wczukcAHi4GXJ30uDoZcHXW42LQt3j994GMIq55bxM5JZVEB3oQ6OVKUm4pmUUVlFSaOZBZDOiZ/sZ6bp3Si1un9MLdpfpv/asq06TCZGFS70Dev34038cm88RPe3j37yOYLRqPnj+gWQHbflvhvPor4lu5ORuIDvLkSFYJCRlFJxXYa5rGorWHefE3NRB1xahuPH/pEJwMeh6Y3p9DmcWs2p/Jvz7bxs8LJhHuq14zKfllzP1oCwczi/FydWLWuO58sO4oS7enkJJXxruzR9l2NHn2133sSysk0NOF168eblfW0ozBYTzx0272pxeRkF7U5GBHVlEFy+PTWLYrlZIKM5eN6sYVo7s5dLeJ3JJKFq45xOcbE1XtEGDm0HBumBTDa38e4J+D2Ty0JJ5/DmXz/KVDHNLWw1nF7EzKx6Cv3mrwRBN7BdIv1JuEjCK+25bEzac1b6BXdE2apslOCidhTUImC76Mo6TSzIBwHz69YYztWO/n4cInc8dyydvr2Z1SyB1fbee92aNwaqQGU0dyJKuYFbvTGdHdj4m9ghzdnHZFp3W0DaUdoLCwEF9fXwoKCvDxafyEytGMRiPLly/nvPPOq7NFmpWmabYgODGnhAuHRXDH2X3oFdzwms2mPPWzKuwV4OnCirtOI7TGiWRSbikz31xHQZmRORN6cNWY7lzy9noqTBbuPacvd57dsm3Pavp0wzHb9kigZtTun9aP2ROiGz1JNJktLPrrMG+sPojRrBHg6cIzFw3m/KGNz56XVJj4Y286P25PZcPh7DoBjquTnkqzBU0DFyc9N06K4fYze+Hj5syGQ9nM/XgrlWYL10/owdMXDqr3w82evrRHudHc5OwrQH5pJde+v5m9aYX0D/PmnetGER3UdFB7sj5ad5T//ra/wXT6aYNCeevakQ0WBfxiU6IthdfPw9k2qAIQ5uPG1IEhFJebOJBRzOGsYipOeJ7oQA+urSpWF+DpwoLFcSzblcagCB9+mj/J7g9Ci0Vj3ufb+HNfJpF+7rx8xTCGdPPFq2qU3Gg08vEPy1mY4EZBmYnpg8J4e9bIFgeemUXl3PTJNuJTCnB10vP61SOYPrj2tnuapvGfX/fZlmE8c9EgZjcykLD2QBZ3fb2d/FIjgZ4uvHXtSCb0qk6JLigz8uD3u/itakeDM/oFE+rtRmpBGSl5ZaTkl9X6/4b5uHHLlJ5cM7Z7nddgUm4pfx3IYv3BbNIKyykorSS/zEhhmZH6NrroGezJmf1C6B/mzZajufx1IIusoqZ3gogO9ODC4ZFcPDyCnnYe42oG9YMifPjy5nG2uhflRjMp+WUczijkpZ9jOVioXh+Rfu48dv4Apg8O44+9GSxYHIfRrHFW/xDenjXS9vfXfL3OnRjNkxcMbPLktrjCxNajuTy6NJ7UgnI+njuGM/uHNPo7t38Zy/L4dB45rz9zJ8ZgtmiYLBYsFrBoGi5Oelyd9LVe35qmkV9q5HhuKYm5pSTllrIzKZ8/9mYAcOuUXjw4vV+t9hZXmLjs7Q0kZBQxONKHb2+ZwLHsUm74ZAsZhRWE+bjx8Q1jGBDuwz8Hs7j9iziKKlQq/0dzx7AvrZDbv4wD4NMbxzKlr/3Fnm7+dBt/7stg/pm9uH9a3RofBaVGftuTxi8709hwOLvO68rTxcDlo7oxZ2I0UX6utuOtGT17UgvYfjyfvWmF9AzyZObQiFY5HmqaxuajuXy7LYkV8emUVdUemNgrkIdm9GdoNz9AHU/e++cIL/+egMmiERXgzhtXj6BHoCfpBeWkF5aRXlBBekEZFg3OHhDC8Ci/Rl9LmqaRlFtGpdlCsJcrPu5OTb72/vdHAm+uPsQZ/YL55IaxDd7v6y3HeWhJPCHeriy6biSjerTuNouapmHRsHupWkOfn0azpdECsx1FpclCZlE5GYUV5JdWUlBmpKDMSH6p+mq2aJw/NJxxMQHNDp4rTRZW7E7ju23JmC0aMcGe9AzyJDrQk5hgT6L8PVqUlVVQamTtwSxW78vgrwNZWCwaY2MCGd8zgPE9AxkQ7tNg/9p7PlRQZmTj4WwqzRrnDgy169ynvTCZLWQUVRDu49boeUFyXinPr9jPr7vUMqRJvQN557pReNcz8Bd3PI9r3ttEhanx882TkV9ayd7UQkwWDW83J3zcndVXN2dcnfR1nq+l57YlFSaWx6fx7bYkth6rrqt17bjuPHLeANu5Vn3KjWZySyoJ93XrkINJzYlDJbC3Q2cK7HenFPDMsr221EgrvU6lNt5xVm+7T36t/tiTzrzPYwH4aO5ozupfd4Z39f4MbvxEVb4P8HQht6SSM/oF89GcMa1SUVnTNO74ajvLdqVxzsBQnr5wULOyAPamFnLfdzvZV7VHdJ8QLwZG+DAg3IeB4eqrn4cz6w5m8+OOFP7Yk2E7IQMI9nZldA9/RvXwZ3R0AAPDfTiQUcSzv+5j45EcAAI9Xbh+QjTv/3OE4goT5w0J481rRp70B1lrKio3svFwDqf1Ca41+9jWzBaNnGJVaT6jUH3NLCwnyMuVa8d1b/JE7Pnl+3i3akY60NOF84aEc8GwCEb38K/1+jJbNJJySzmQUcS6Q9ksjUuhqELN6rsY9EzoFcjaA1kY9Dp+mj+pwZ0CGlJYbuSit9bbig3qqnZ4GBrpy4BwL95ZtZ+cCh3Do/z46l/jT/p/XFJh4s6vtrNqv6ph4WzQ4aTX46TXYTDo0Ot0tnX4z14ymFnjmq4gnpRbyi2fx7I3rRCDXsfDM/pz0+QY4lMKmL84jqTcMpwNOh49bwBzJkbX+pDUNI3s4kqWx6ex6K/DpFet7QvycmXe6TH0D/Nh7YEs/krI5HBWSaPt8HQxMLKHP2f1D+HMfiF1giqLRWNvWiF/JWSyJiGLQ5nFVJoslJvMNPSpNizKj0uGRzBzWARBDRRmS0hX6fc5JZUMjvThi5vG1VvM0mg08uuvy9H3GMkLvx8kJb8MgOFRfsSnFKgT6yHhvHrV8DonwV9tOc7DS+IBuG5893qzNrKKKth4OIcNh7PZmaweD9SxetPDZzc5C//anwd47c+Djd4HVKDk5qTH1dmA0WSxvR9O9Nj5AxqciU3KLeXihevJKalkXEwAe1ILKa4w0TfUi09uGFvrWHwgo4gbPt5KSn4Zfh7OmKsyNG47o5fdBTitftmZyh1fbaebvzv/PHAmAIezSvgrIZO1B7LYdCSn1qDrsCg/LhwWgauTnk83HONgZnWxtyl9grAUZZJv8GN/elG92SjDuvlywbAIzh8abstMsFdqfhk/xKrigMdrbB06MNyHB2f05/Q+QfWecG4/nscdX20nOa+syefo5u/OzKERXDAsnIHhPuh0OgrLjWw4lMPaA1n8fSDL9joFdbwI9HQl0MuFYG9XBob7MDYmgFE9/PF2c8Zi0Tj9pTUk55Xx+tXDuWh4ZIPPXW40M/WVtbZ2Xj6qGw/N6N/g+6wxRrOFw1nF7E0tZE9qIXtTC9mbpl5TUf7uxAR5EhPkRUywJzGBnoT4uNoyddyc1VfNYuKLH38jpO9IEjJL2JdWyL60QjIKK5jcO4jbz+zFhJ6BDZ7ka5o6vhzOKkGHOpbrdbqq73WYLRpGs4VKk4XKqq8mi4U+Id6MiQloNMgAyCwsV4WB/dwI9nKttx0ms4VDWcXEJxewJ7WQ5LxS0gvLSS+oINvO7W2HR/lx65RenDswtMlzrayiCr7acpwvNiWS2cigqUGvo5utH6ovkX7uWDSNcqOFCpPZ9vVQZjGr9mWyLTHPdhyrj7ebE+NiAugX5k24r1reFeHnTrivO+4GjRUrVtQ5H7JYNPakFrL2gHrPxx3Ptz1HoKcLcydGM3tCD7sLEjdXUbmRrcdy2XREbdU8PMqf0T386RHoYVcAmVVUYftM/OdgNgVlRrr5u3PpiEguGdnNVssE1Of9O2sP897fR6gwWdDr4LrxPXjs/IGNDrSsiE/j9sVxaJo63kT6uxPu60aYrxthPm4Ee7ui1+mwaBqaBhpq8FeHmphyMejVVyc9zgY9GQXl7EopID65gF0p+STlNnxscjHoawX73m5OeLkYKMpJZ/q4QYyKVtk+9U2gaJpGZlEF+9OLWL6rKtuqUp1363UwpJsfO5PyAXXse/HyoXVm77OKKvh8UyJfbkokp6SSIZG+3HxaDOcNCe9QA3wS2LeyjhLYbz2Wy3O/7sWpPI9JQ/vSO9RbjbAGeVJUbuKl3xNYsj3ZNov8r9NiOKNfCO+uPcyf+1RgoNfBxSMiuWJUFG7Oegx6XfVFp6Ow3ERWkfpQsX5dtiuNgjIjN0+O4bGqSvX1een3/bZia5F+7vx65+RWPdhaLBrpheUtTuuvNFl4a/VBFlatgT2Ri5O+1qxydKAHFw2P5IJhEfQK9qz3IK5pGqv2ZfLc8n22nQIAxsUE8OmNYxsdTXZEYN9RWSway+LT8PdwZkLPQLtn2UsrTfyyM5UvNx9nV3KB7fpbpvTk4RktW2aQmFPCi78nsD0xz7ZNY01R/u4snT+pRSe89bFul/j5psR6A1qDXsd/Lh7MNc1Y7lFWaeaRpfEsrVpbO75nAHGJ+VSaLUQFuPPWNSMZdsJ66hNVmMx8H5vM22sO1womarZrZHc/zugXQr9Qb3w9nPFzd8bXwxlfd+cWr53XNA2TRaPCZKG00sSGQzks3Z7CPwezbDO2Br2Ood18GdbNj+FRfgyL8iM60IMDGcV2BfVQ+/1p0vQsWnuYd9Yeth0jLh0ZyYuXDW3wtfjt1iQeXLKrwUGIE3UP8GBir0DOHxpu1xZGO5PyueKdjbYU7+YI9XGle4AH3QM81fP2DmRMdOMzsFuP5XLt+5tsAfH4ngG8O3t0veuts4oquPmzbbaTslE9/Pl63vhmn2iVVZoZ9Z+VlFaamTk0nJ3JdU8y+4d5c8GwCC4YGlGr5ommaaw/lMPH64+yOiGzTj8EebkwPMqfgeHebE/KZ/2h6hl/nQ5GRPnh6epEhVENJpUbVSBTsxCo9RNBp9ORWlBmew5PFwMXDIvgitHdGNndv8kAoKDMyKNL41lWNUMX5OVCqI86KQ/zdaOw3MSqfRmUVlYPNvcM9iTQ06VWoAPqRNvVWV/vMiUrvQ4GRvjQO9iLH3ek4uXqxNZHpzY5EJldXMGLv+3n222qtou3mxP/Prcfs8Z1t70PUvLLiK1athafUkBJhckWGFsD5ZIKc4tet801orsft5/Rm7P7h9iC3qPZJfy8I5Wfd6Y0OfjYECe9jmFRfkzqFcjE3kEMifTlcFZx1VK9fOIS82odE92c9VXvNw+iAjwwmTXiUwrYl1ZYJ8OsJheDnhAfVwI8XfB1d8bHXR07fd2dyS2uZOmOFNvxqGewJ7ec3pOLq3Y1KCgzUlA1u59bUslve9JZtjPN9n8P8XZl1rgedPN351hOCUeySziaVcKxnJJar7Pm6hvqxVn9Qzl7QAjOBj2bj+Sw+WguW47mUtzAoCKAh4sBncWEh5srTgZ1buqk11FQZiTvhMLJvYI9KTdabP9jDxcDV42J4qbJMXTz98Bi0cgpqSS9oJzUgjLSC8rJLanOelCZD5WUVJgJ9HIhzNetKhB2r5pNh81Hc9l0OIf4lIJ6M8yCvNRkz+hof4K9XW3HiQqjhXKjmcJyI5uO5BKfUlD3l2sYHuXHZSMjcXM28PIfCWQUqgGX8T0DeGLmoFrLTBvzwT9HbEsM20J0oAfuLk4UlhkpKjdSVGGy+7PNzVnPkEhfhkf5EeztyuHMEg5mFnEws7jOcapHoAdXjo7ispHdCPN1Y8OhbO7/fpetr+dM6MGDM/pzPLeUD/85yk87Uus9loT7ujF3YjRXj+3eIWqCSGDfgIULF/LSSy+Rnp7OsGHDePPNNxk7tuHUMquOEth/vimRx6tSPE9k0OtsH+4XDY/g/mn9ahWy25Wcz+t/HrTN/DXX0G6+fH/rxEZHDU1mCwsWb2dbYh4fzR1tSzlsb7KKKohPyWdfWhF7U9Uo/9GcEjRNjQBfMCyCi4ZHNJn2WJPRbGHx5uO8ufog3fw9+PTGsU0eTCSwP7Xikwv4autxKk0WnrlocKtkLGQVVbA7pYBdyQXsTMojMTWTt2+YTL8Iv5Nv8AkKSo2UGk2YzCqwNVssmCxqeYl1q77msK6l/8+v+2zr26cNCuXFy4c164PQaLawNC7FlqkyuXcQZ/QLYXKfoFP6gZpZVM6ynWn8uCOl1iCOla+7mqEsqjA1GdRD/e/PpNxS3lp9iHA/N+48q0+TM2Q/7Ujh5T8SKK2oe6Ls7mJgTHQAE3oFMrFXYK3jtb1KK02UGy22k2DrIK0OMJq1WrNq1hmgSD+PFr/2l8Ql8/CSeM4fEs7zJxTIO1FZpZknftrNgcxi3p41ksgWDsje880O2wAUqGBnbEwAZ/QL5ox+IfQOaToD7Vh2CV9tTmT/ocNcPHkYo2OC6ObvXuv4nlVUwYrdafyyM7VWGmhzjIsJ4MrRUcwYEoaHS/NLHBWUGnFz0df7fy2rNLMmIZNfdqayen9mrYCwZ5Anp/cN5vS+QYzvGYiHi5MtNTW7uIKc4krSCsqJO57HlqO5tTIKQM2+v3zFMLvbGXc8jyd+2s3uFJUBNzDch57BnsQm5pFm59aaXq5ODAz3YWCEj+1rgKcLx3JKOJpdwrFs9fVIdgn5pUYqjOo1XLMWh4teY2CkHwMjfKuy77zxdXfms42JfL01yRb09gv1ZtqgUP46kFXr2ODipGd4Nz/0elTwpoFG9ZIA16oZTBeDHueqc5+dSfl1/n/10ekg2MuV7OKKegPDWv+HCB8GR/jSK8TTNpgT5uNGgKdLo+cgWUUVfLLhKJ9vTKSwKkBy0utq/Y9ONDzKjxsmRTNjcHi953PWWdQjVUH+0ewS2/dp+WU4O+lxq6pxYv0a6OnClL7BnNU/tMGCwiazhb1phbbXX2p+Gan5KvDOL218S1svVycm9gpkSr9gTu8TXDU4YuHX+DTeWXvElolp0OuI8HMjo6CiVQeOegR6MD4mEB93tTtOfEpBvRk/DRkc6cMZfUM4o18w/cN9WLUvgyVxtQejraIC3Hn0vAFMGxTW7JTyo9klHM4sJq2wnPSCqqU8hWXk1CgOq9Pp0OvU61PTqDXgVmm2UGGy4OfhzNBIP4Z082VopC+DIn3rfJZbLBollSaKyk0UlhvV1zL1Nb+knPXb91LiFkR8cmGDWWKgBhmjAz0Z2cOfK0Z1Y2w9S0uKK0w8t3wfizcfB+ouyRwe5cfNp8UwNjqAr7cm8dnGY7aCuJ4uBq4cE8W/TuvZ4knBU0EC+3p88803XH/99bzzzjuMGzeO1157je+++46EhARCQhpfq9hRAvvU/DK2Hc3m9w3bcQmK4nhuGUezS2ypuKN7+PPYzIF1KhfXtDMpn4VrDrE/vQizRcOiaZgtVRdNw8vViSAvV4K91SXIy5VwXzcuGBbRZOqZlcls6XAFPEorTaTklREd5HlS6TtaVaqTPcsPJLDvXDpqf245msvrqw5w7sAwrp/Qo0OuTzvR8ZxS4o7nsSMpn13J+exOLbSd5A+O9OHLm8Y3WVS0o/ZnW6s0WVp9V4TGHM4q5omfdhMT5MkZfUOY0CuwRdWfm9OfKfllbDycg16nChW62YIYAy4Gve2kGFQgCGoG71SdOBZXqBn8skozk3oH1Sne2pT0gnK2HMtly9Ec0vLLeeKCgXYVEa3JbNFYvOU4L/+eQEFZ9Um2Qa9jUIQPo3r4M6K7PwEeLlUpvjqcDarug7uLgQhf9xYt0zNVBR8l5RWsX/MnM8+vvz8zi8r5aN0xvtiUWGum2KDXMal3EBcOi+DcQaEtKliYlFvKxsM5rD+czfpDOWQXV+Dj5sTIHv6M7K4uw6J88XZzptJkITW/jOO5pbaLDpUxMSTSl+hAz5NerlhcYeKrzcf5cN1R2/IonU4VzvWrypTqHeLNdeO7M6KBIqyOVFppIiW3mFVr1jJx0mno9AZMFgtmi4azQc+AcJ8GjzmapvHPwWze/fsw6w/l2K7X6arek1Up6YFeripjzF39T3zdXfBwMZBTUkFaQbma3c9XtS3KKs2M7O7PhF6BjO8ZWOd9XW40E59SwLZjecQm5lFSYVLHCGcDbs6qwKubs4HBkb6c3jeowYH3zKJyft6RypK4FLKLK5gzMZqbJsd0qLoB9al5rDUYnDiSXcKOpHx2JOWRV2qkV7AXfUK86BPqRXSgp91/798Hsnjwh12kFZSj18GMweHcODmGUT1qv6bLjWZ+3pHKB+uOcCBDLcf67tYJTWalOZIE9vUYN24cY8aM4a233gLAYrEQFRXFHXfcwUMPPdTo73aUwB7qPzkpKDWSX1ZJ9wD71vyI9kECh85F+rP9MpotJKQXcTS7hDP7h9g1SCn92blIf7aN3JJKvtiUiF4Ho3oEMCzKt0XZCs3VnGJrX2xKJD65gIm9AzlvSHirLZOC6rojgZ4urVJP6GQYzRbSC8rxcVPrnR3dnuZojffnwYwi8kqNhPuqnXdO5QCkqNaWx9rCciMr92QwNiagyQFNTdP4+2A2a/Zn2lXE1pGaE4d2ie3uKisriY2N5eGHH7Zdp9frmTp1Khs3bqxz/4qKCioqqguHFBaqNB6j0YjR2HhKkKNZ21eznR7O4OHsgsnUcLqLaH/q60vRcUl/tm/9QjzoF+IBaHb1kfRn5yL92Ta8XXTcdnp0jWvse3+dLHv708MJ5k2uXVS0tdvn56bHbDZhbvnS9FYT5q0CqfbSHnu1xvszOsCN6ICq2XHNjNHYgf4BnUhbHmvdDXDh0FC7H39ijB8TY/zafXzUnP9Vl5ixT01NJTIykg0bNjBhwgTb9Q888ABr165l8+bNte7/1FNP8fTTT9d5nMWLF+Ph0fx1jkIIIYQQQgghRHOUlpZy7bXXyox9Sz388MPce++9tp8LCwuJiori3HPP7RCp+CtXruScc86RdMIOTvqyc5H+7FykPzsX6c/ORfqzc5H+7DykL5vPmjlujy4R2AcFBWEwGMjIyKh1fUZGBmFhYXXu7+rqiqtr3TVWzs7OHeZF2JHaKhonfdm5SH92LtKfnYv0Z+ci/dm5SH92HtKX9mvO/6lLVI5wcXFh1KhRrFq1ynadxWJh1apVtVLzhRBCCCGEEEKIjqZLzNgD3HvvvcyZM4fRo0czduxYXnvtNUpKSrjhhhsc3TQhhBBCCCGEEKLFukxgf9VVV5GVlcUTTzxBeno6w4cP57fffiM0NNTRTRNCCCGEEEIIIVqsywT2AAsWLGDBggWOboYQQgghhBBCCNFqusQaeyGEEEIIIYQQorOSwF4IIYQQQgghhOjAJLAXQgghhBBCCCE6MAnshRBCCCGEEEKIDkwCeyGEEEIIIYQQogOTwF4IIYQQQgghhOjAJLAXQgghhBBCCCE6MAnshRBCCCGEEEKIDkwCeyGEEEIIIYQQogOTwF4IIYQQQgghhOjAJLAXQgghhBBCCCE6MCdHN6Aj0DQNgMLCQge3pGlGo5HS0lIKCwtxdnZ2dHPESZC+7FykPzsX6c/ORfqzc5H+7FykPzsP6cvms8af1ni0MRLY26GoqAiAqKgoB7dECCGEEEIIIURXUlRUhK+vb6P30Wn2hP9dnMViITU1FW9vb3Q6naOb06jCwkKioqJISkrCx8fH0c0RJ0H6snOR/uxcpD87F+nPzkX6s3OR/uw8pC+bT9M0ioqKiIiIQK9vfBW9zNjbQa/X061bN0c3o1l8fHzkDdNJSF92LtKfnYv0Z+ci/dm5SH92LtKfnYf0ZfM0NVNvJcXzhBBCCCGEEEKIDkwCeyGEEEIIIYQQogOTwL6TcXV15cknn8TV1dXRTREnSfqyc5H+7FykPzsX6c/ORfqzc5H+7DykL9uWFM8TQgghhBBCCCE6MJmxF0IIIYQQQgghOjAJ7IUQQgghhBBCiA5MAnshhBBCCCGEEKIDk8BeCCGEEEIIIYTowCSw70QWLlxIdHQ0bm5ujBs3ji1btji6ScIOzz//PGPGjMHb25uQkBAuvvhiEhISat2nvLyc+fPnExgYiJeXF5dddhkZGRkOarGw13//+190Oh1333237Trpy44lJSWF6667jsDAQNzd3RkyZAjbtm2z3a5pGk888QTh4eG4u7szdepUDh486MAWi4aYzWYef/xxYmJicHd3p1evXjzzzDPUrCEs/dl+/f3331xwwQVERESg0+n48ccfa91uT9/l5uYya9YsfHx88PPz46abbqK4uPgU/hXCqrH+NBqNPPjggwwZMgRPT08iIiK4/vrrSU1NrfUY0p/tR1Pvz5puvfVWdDodr732Wq3rpT9PngT2ncQ333zDvffey5NPPklcXBzDhg1j2rRpZGZmOrppoglr165l/vz5bNq0iZUrV2I0Gjn33HMpKSmx3eeee+7hl19+4bvvvmPt2rWkpqZy6aWXOrDVoilbt27l3XffZejQobWul77sOPLy8pg0aRLOzs6sWLGCvXv38r///Q9/f3/bfV588UXeeOMN3nnnHTZv3oynpyfTpk2jvLzcgS0X9XnhhRdYtGgRb731Fvv27eOFF17gxRdf5M0337TdR/qz/SopKWHYsGEsXLiw3tvt6btZs2axZ88eVq5cybJly/j777+ZN2/eqfoTRA2N9WdpaSlxcXE8/vjjxMXFsWTJEhISErjwwgtr3U/6s/1o6v1ptXTpUjZt2kRERESd26Q/W4EmOoWxY8dq8+fPt/1sNpu1iIgI7fnnn3dgq0RLZGZmaoC2du1aTdM0LT8/X3N2dta+++4723327dunAdrGjRsd1UzRiKKiIq1Pnz7aypUrtSlTpmh33XWXpmnSlx3Ngw8+qE2ePLnB2y0WixYWFqa99NJLtuvy8/M1V1dX7auvvjoVTRTNcP7552s33nhjresuvfRSbdasWZqmSX92JIC2dOlS28/29N3evXs1QNu6davtPitWrNB0Op2WkpJyytou6jqxP+uzZcsWDdASExM1TZP+bM8a6s/k5GQtMjJS2717t9ajRw/t1Vdftd0m/dk6ZMa+E6isrCQ2NpapU6fartPr9UydOpWNGzc6sGWiJQoKCgAICAgAIDY2FqPRWKt/+/fvT/fu3aV/26n58+dz/vnn1+ozkL7saH7++WdGjx7NFVdcQUhICCNGjOD999+33X706FHS09Nr9aevry/jxo2T/myHJk6cyKpVqzhw4AAAO3fuZN26dcyYMQOQ/uzI7Om7jRs34ufnx+jRo233mTp1Knq9ns2bN5/yNovmKSgoQKfT4efnB0h/djQWi4XZs2dz//33M2jQoDq3S3+2DidHN0CcvOzsbMxmM6GhobWuDw0NZf/+/Q5qlWgJi8XC3XffzaRJkxg8eDAA6enpuLi42D7MrEJDQ0lPT3dAK0Vjvv76a+Li4ti6dWud26QvO5YjR46waNEi7r33Xh555BG2bt3KnXfeiYuLC3PmzLH1WX3HXunP9uehhx6isLCQ/v37YzAYMJvNPPvss8yaNQtA+rMDs6fv0tPTCQkJqXW7k5MTAQEB0r/tXHl5OQ8++CDXXHMNPj4+gPRnR/PCCy/g5OTEnXfeWe/t0p+tQwJ7IdqR+fPns3v3btatW+fopogWSEpK4q677mLlypW4ubk5ujniJFksFkaPHs1zzz0HwIgRI9i9ezfvvPMOc+bMcXDrRHN9++23fPnllyxevJhBgwaxY8cO7r77biIiIqQ/hWinjEYjV155JZqmsWjRIkc3R7RAbGwsr7/+OnFxceh0Okc3p1OTVPxOICgoCIPBUKeydkZGBmFhYQ5qlWiuBQsWsGzZMtasWUO3bt1s14eFhVFZWUl+fn6t+0v/tj+xsbFkZmYycuRInJyccHJyYu3atbzxxhs4OTkRGhoqfdmBhIeHM3DgwFrXDRgwgOPHjwPY+kyOvR3D/fffz0MPPcTVV1/NkCFDmD17Nvfccw/PP/88IP3ZkdnTd2FhYXUKCptMJnJzc6V/2ylrUJ+YmMjKlStts/Ug/dmR/PPPP2RmZtK9e3fbuVFiYiL33Xcf0dHRgPRna5HAvhNwcXFh1KhRrFq1ynadxWJh1apVTJgwwYEtE/bQNI0FCxawdOlSVq9eTUxMTK3bR40ahbOzc63+TUhI4Pjx49K/7czZZ59NfHw8O3bssF1Gjx7NrFmzbN9LX3YckyZNqrP15IEDB+jRowcAMTExhIWF1erPwsJCNm/eLP3ZDpWWlqLX1z7tMRgMWCwWQPqzI7On7yZMmEB+fj6xsbG2+6xevRqLxcK4ceNOeZtF46xB/cGDB/nzzz8JDAysdbv0Z8cxe/Zsdu3aVevcKCIigvvvv5/ff/8dkP5sNY6u3idax9dff625urpqn3zyibZ3715t3rx5mp+fn5aenu7opokm3HbbbZqvr6/2119/aWlpabZLaWmp7T633nqr1r17d2316tXatm3btAkTJmgTJkxwYKuFvWpWxdc06cuOZMuWLZqTk5P27LPPagcPHtS+/PJLzcPDQ/viiy9s9/nvf/+r+fn5aT/99JO2a9cu7aKLLtJiYmK0srIyB7Zc1GfOnDlaZGSktmzZMu3o0aPakiVLtKCgIO2BBx6w3Uf6s/0qKirStm/frm3fvl0DtFdeeUXbvn27rUq6PX03ffp0bcSIEdrmzZu1devWaX369NGuueYaR/1JXVpj/VlZWaldeOGFWrdu3bQdO3bUOjeqqKiwPYb0Z/vR1PvzRCdWxdc06c/WIIF9J/Lmm29q3bt311xcXLSxY8dqmzZtcnSThB2Aei8ff/yx7T5lZWXa7bffrvn7+2seHh7aJZdcoqWlpTmu0cJuJwb20pcdyy+//KINHjxYc3V11fr376+99957tW63WCza448/roWGhmqurq7a2WefrSUkJDiotaIxhYWF2l133aV1795dc3Nz03r27Kk9+uijtQIF6c/2a82aNfV+Vs6ZM0fTNPv6LicnR7vmmms0Ly8vzcfHR7vhhhu0oqIiB/w1orH+PHr0aIPnRmvWrLE9hvRn+9HU+/NE9QX20p8nT6dpmnYqMgOEEEIIIYQQQgjR+mSNvRBCCCGEEEII0YFJYC+EEEIIIYQQQnRgEtgLIYQQQgghhBAdmAT2QgghhBBCCCFEByaBvRBCCCGEEEII0YFJYC+EEEIIIYQQQnRgEtgLIYQQQgghhBAdmAT2QgghhBBCCCFEByaBvRBCCCHaBZ1Ox48//ujoZgghhBAdjgT2QgghhGDu3LnodLo6l+nTpzu6aUIIIYRogpOjGyCEEEKI9mH69Ol8/PHHta5zdXV1UGuEEEIIYS+ZsRdCCCEEoIL4sLCwWhd/f39ApckvWrSIGTNm4O7uTs+ePfn+++9r/X58fDxnnXUW7u7uBAYGMm/ePIqLi2vd56OPPmLQoEG4uroSHh7OggULat2enZ3NJZdcgoeHB3369OHnn3+23ZaXl8esWbMIDg7G3d2dPn361BmIEEIIIboiCeyFEEIIYZfHH3+cyy67jJ07dzJr1iyuvvpq9u3bB0BJSQnTpk3D39+frVu38t133/Hnn3/WCtwXLVrE/PnzmTdvHvHx8fz888/07t271nM8/fTTXHnllezatYvzzjuPWbNmkZuba3v+vXv3smLFCvbt28eiRYsICgo6df8AIYQQop3SaZqmOboRQgghhHCsuXPn8sUXX+Dm5lbr+kceeYRHHnkEnU7HrbfeyqJFi2y3jR8/npEjR/L222/z/vvv8+CDD5KUlISnpycAy5cv54ILLiA1NZXQ0FAiIyO54YYb+M9//lNvG3Q6HY899hjPPPMMoAYLvLy8WLFiBdOnT+fCCy8kKCiIjz76qI3+C0IIIUTHJGvshRBCCAHAmWeeWStwBwgICLB9P2HChFq3TZgwgR07dgCwb98+hg0bZgvqASZNmoTFYiEhIQGdTkdqaipnn312o20YOnSo7XtPT098fHzIzMwE4LbbbuOyyy4jLi6Oc889l4svvpiJEye26G8VQgghOhMJ7IUQQggBqED6xNT41uLu7m7X/ZydnWv9rNPpsFgsAMyYMYPExESWL1/OypUrOfvss5k/fz4vv/xyq7dXCCGE6Ehkjb0QQggh7LJp06Y6Pw8YMACAAQMGsHPnTkpKSmy3r1+/Hr1eT79+/fD29iY6OppVq1adVBuCg4OZM2cOX3zxBa+99hrvvffeST2eEEII0RnIjL0QQgghAKioqCA9Pb3WdU5OTrYCdd999x2jR49m8uTJfPnll2zZv76PKwAAAZFJREFUsoUPP/wQgFmzZvHkk08yZ84cnnrqKbKysrjjjjuYPXs2oaGhADz11FPceuuthISEMGPGDIqKili/fj133HGHXe174oknGDVqFIMGDaKiooJly5bZBhaEEEKIrkwCeyGEEEIA8NtvvxEeHl7run79+rF//35AVaz/+uuvuf322wkPD+err75i4MCBAHh4ePD7779z1113MWbMGDw8PLjssst45ZVXbI81Z84cysvLefXVV/n3v/9NUFAQl19+ud3tc3Fx4eGHH+bYsWO4u7tz2mmn8fXXX7fCXy6EEEJ0bFIVXwghhBBN0ul0LF26lIsvvtjRTRFCCCHECWSNvRBCCCGEEEII0YFJYC+EEEIIIYQQQnRgssZeCCGEEE2SlXtCCCFE+yUz9kIIIYQQQgghRAcmgb0QQgghhBBCCNGBSWAvhBBCCCGEEEJ0YBLYCyGEEEIIIYQQHZgE9kIIIYQQQgghRAcmgb0QQgghhBBCCNGBSWAvhBBCCCGEEEJ0YBLYCyGEEEIIIYQQHdj/A1K3+BNnAvPBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印时间\n",
    "print(f\"Training time: {train_time} seconds\")\n",
    "print(f\"Testing time: {test_time} seconds\")\n",
    "\n",
    "# 绘制训练和验证损失\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Train loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.title('Training and Validation Loss Over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
